Category: Semiconductor Stocks

AMD Update: The One Critical Reason I’m Still Feeling Zen

Posted on by io-fund

Below, we look at the PC-related miss and what to expect from AMD in the data center.

The data center is critical right now because this is what can help AMD reclaim its stock price. I wouldn’t dismiss the possibility that this year becomes AMD’s best year in the data center as the company is perfectly positioned to make a substantial increase in market share over Intel. By best year I am not referring to growth percentage but rather judging by impact of total revenue and size of this segment. The move from AMD owning “mid-20%” of the CPU data center to owning 40% to 50% of the market is the move we want to capture — and this is entirely possible due to Intel’s most recent stumble.

Arm vs x86 Server Market Share Q2 2022

Source: Tom’s HardwareTom’s Hardware

Pictured Above: AMD has grown from 2% market share to “mid 20-percent market share”

It’s important to note that AMD has grown over 6% of its market share in the last two quarters. Per our recent Q2 coverage: As an analyst pointed out, AMD appears to have gained 6% market share, which is “the highest share gain in the data center business that [AMD] has reported even going back to 2005.”

We have been quite thrilled to see the team at AMD led by Lisa Su and Forrest Norrod overtake Intel at times. However, what happened last quarter with Intel’s stumble is an exponentially greater mistake than the last stumble that we prepared for in March of 2020, which later materialized in July of 2020.

You may have seen tech commentary poking fun of Meta’s most recent keynote on social media and in newsletters. If you missed it, the recap is that Mark Zuckerberg demonstrated adding legs for Metaverse avatars and quickly became a target as the progress doesn’t quite reflect the company’s large level of investment.

Most investors look at Facebook’s cash and think “this will make a great stock,” which is first level thinking — yes, the FCF margin is impressive. Some are taking this further and scrutinizing “where is the cash being spent and will Meta succeed?”

However, the most important takeaway to Meta’s insatiable appetite to find the next big thing in media is that AMD stands to greatly benefit whether Meta succeeds or not.

Here is data presented on the forum from last year that shows Meta’s increased capex following the Q3 2021 earnings call.

FB Annual Capex

We don’t get enough transparency to separate real estate costs from servers and data centers in the capex number but we do know that Meta chose AMD to build its data centers – which was an earth shattering announcement for AMD investors. Amazon did at one point comment about its split and said 40% of its capex is spent on data centers.

This is precisely why AMD leap frogging Intel is critical for investors to not forget or lose sight of in a consumer led selloff. Meta capex spending with AMD can grow to exceed AMD's PC revenue in the next 2-3 years alone, and of course, AMD is partnered with many hyperscalers beyond Meta.

The decision to choose AMD at Meta’s primary supplier was very likely due to AMD’s historic product lead over Intel, which we have covered in this webinar and this write-up. Point being, not only is AMD in the lead on product but AMD is in the lead at exactly the right time. Meta helps to quantify the impact.

In regard to Nvidia, one thing to remember, is that Nvidia’s Omniverse creeps onto Meta’s territory for building 3D Worlds. I imagine Meta’s ultimate goal is to attract creators and developers to build their 3D apps with Meta and this is also Nvidia’s Omniverse’s goal. AMD does not compete here and so it’s an obvious choice for Meta to partner with AMD on both CPUs and GPUs.

Brief Note on Data Center Numbers:

There is additional evidence of the impact of Intel’s previous stumble as AMD’s data center segment grew 83% year-over-year in Q2 compared to Intel’s (16%) decline. Intel has not caught up to the AMD’s products released two years ago and now AMD is being even more aggressive as the 5-nanometer line up is being released in Q4 which includes Zen-4 architecture plus the Zen-5 architecture in 2024. We covered the upcoming product road map here.

The company also stated that the Zen-3 Milan Series is still outstripping supply with visibility six quarters out, implying for full year 2023. As a reminder, Zen-2 was Lisa Su’s comeback and Zen-3 is responsible for the current move in data center market share.

However, as an AMD investor, I am actually less concerned with shiny numbers like 83% growth and I am more concerned with the data center segment becoming so large in total revenue that more cyclical segments like PCs have less impact on the stock.

Data center revenue is a $6 billion segment with Q2 data center revenue at $1.5 billion and for Q3 is $1.6 billion. AMD did not break out the data center number before but judging by the 83% in Q2 and the 45% in Q3, the company looks to be tracking in the ballpark of Meta’s capex number of 66% (I find the average growth for AMD to be roughly close to Meta’s capex if we assume Q1 was in line with Q2 and that Q4 will be roughly in line with Q3 to be interesting although there isn’t enough information about Meta’s orders to verify the correlation).

If Meta reports 30% growth in capex next year, the data center segment can easily become $7.8 billion next year. However, what this overlooks is the impact of AMD taking more market share from Intel, which will likely be seen when AMD attracts higher budgets from more hyperscalers. If AMD takes another 10% to 20% of market share over the next 18 months, we could see roughly a $9 billion to $10 billion segment, which would be 60% growth over 1.5 years (or 40% average annual growth). This is entirely possible as AMD is taking 12% market share this year, and again, I believe AMD eating into Intel’s market will actually accelerate more this year than it did last year. I am using the word “year” loosely to say Q4 2022 through the end of fiscal 2023, so about 6 quarters.

I do want to point out that Arm is starting to creep into X86 server territory as evidenced by the chart above, however, I believe this is a bigger problem for Intel and not as much for AMD who has clearly not been impacted by Arm’s growth.

More on Big Tech Capex

Royston wrote about Big Tech Capex on the forum and some of this is going out as our free newsletter this week. Here is what he wrote:

Our team has been working on looking closer at AMD and here is some research from Big Tech Capex, which was a leading indicator going into 2022. Our goal is to see if there's any sign of slowing data center and server investments from the Big 4.
Monitoring the capital expenditures of big tech companies helps to understand the demand for data centers and artificial intelligence. While not all Capex goes to data centers and AI, it remains a significant investment in recent years.

Meta’s capital expenditures in Q2, including principal payments on finance leases were $7.75 billion, up 64% YoY. The company’s CFO, Dave Wehner, said in the Q2 earnings call, “Capital expenditures, including principal payments on finance leases, were $7.7 billion, driven by investments in servers, data centers and network infrastructure. The big step-up in CapEx, both year-over-year and sequentially related to server spend, including for our AI infrastructure.”driven by investments in servers, data centers and network infrastructure. The big step-up in CapEx, both year-over-year and sequentially related to server spend, including for our AI infrastructure.”

Sheryl Sandberg said, “Third, on AI and machine learning. I want to emphasize Mark's point that this is a really important part of how we improve our ads ranking and measurement capabilities. AI-driven products like advantage detailed targeting and advantaged look-alikes help to increase the audience for an ad campaign if it's likely to improve performance. AI is also an important part of how we continue to grow video monetization.”AI and machine learning. I want to emphasize Mark's point that this is a really important part of how we improve our ads ranking and measurement capabilities. AI-driven products like advantage detailed targeting and advantaged look-alikes help to increase the audience for an ad campaign if it's likely to improve performance. AI is also an important part of how we continue to grow video monetization.”

The company expects 2022 capital expenditures, including principal payments on financial leases, to be $32 billion at the mid-point of the guidance, representing a 66% YoY growth. Tracking the Capex in the first two quarters, Meta Platforms had spent $13.3 billion. It suggests it will be higher in 2H 2022, as when we deduct from the mid-point of the guidance, it comes to $18.7 billion.

The company is planning to reduce the hiring of engineers by 30% this year due to the economic slowdown according to an internal memo. The company is facing the heat due to privacy issues and slowing ad revenues. The memo suggests a five-fold increase in the requirement of GPUs to make its feed better to increase engagement using Artificial Intelligence as the company is trying to combat the competition from TikTok.

Meta also recently announced its plan to expand the Eagle Mountain data center project. It is Phase 3 expansion plan and brings the total investment in the project to over $1.5 billion.

Alphabet’s Q2 Capex grew by 24% YoY to $6.9 billion. Ruth Porat, CFO of Alphabet, said, “Turning to CapEx. The largest investments in the second quarter were in servers followed by data centers and office facilities.” were in servers followed by data centers and office facilities.” The company had invested $24.6 billion in Capex in the year 2021, up 11% YoY. The management expects Capex to rise in 2022. In the Q2 2022 earnings call, Ruth Porat said, “We continue to expect an increase in CapEx in 2022 versus last year. For the balance of 2022, the increase will be particularly reflected in investments in technical infrastructure globally with servers as the largest component.” Earlier this year, the company announced its plan to invest about $9.5 billion in data centers and offices in the U.S. for the year 2022. This is up from about $7 billion spent in 2021.

Similarly, Microsoft’s Capex including financial leases, grew by 19% YoY to $8.7 billion in the Q4 FY2022 quarter (i.e., Q2 CY2022). Amy Hood, CFO of Microsoft, said, “Maybe let me start by talking about Q4's capital spend. Obviously, the big driver of our growth this quarter was in data center spend, both new and newbuilds as well as adding capacity to existing data centers. We are seeing, obviously, good demand signal.” data center spend, both new and newbuilds as well as adding capacity to existing data centers. We are seeing, obviously, good demand signal.” The management expects a sequential decrease in the next quarter due to the normal variability in quarterly spend. In the CY 2021, Microsoft’s Capex including financial leases, grew by 33% YoY to $27.5 billion.

Amazon incurred capital expenditures, including equipment financial leases, of about $60 billion in 2021. About 40% of this is made up of technology infrastructure supporting AWS and worldwide stores business. The management expects Capex to increase over the last year with the increase in technology infrastructure.

Brian Olsavsky, senior VP and CFO, said in the Q2 2022 earnings call, “For full-year 2022, we do expect to spend slightly more on capital investments than last year, but the proportion of capital spending shifts among our businesses. We expect technology infrastructure spend to grow year-over-year, primarily to support the rapid growth in innovation we are seeing with AWS. We expect infrastructure to represent a bit more than half of our total capital investments in 2022.”

AMD’s PC-Miss

As you already know, AMD pre-announced earnings with a miss of $1.1 billion for revenue of $5.6 billion, down from previous guidance of $6.7 billion. This will represent growth of 29% compared to growth of 55% at the mid-point of the guidance that was previously expected.

The low margins would also imply that average sales price is coming down quite a bit in addition to number of units. Here is what IDC said: “Shortages over the last several years have aggressively driven product mix shifts towards the premium end. This, coupled with cost increases of components and logistics, drove ASPs up five quarters in a row to $910 in 1Q22, the highest since 2004. However, with demand slowing, promotions in full swing, and orders being cut, the ASP climb was reversed in 2Q22. Another quarter of ASP declines indicates a market in retreat."

Although AMD has a clear lead ahead of Intel in the data center (where it matters most for long-term investors) this is not necessarily the case in PCs. According to Tom’s Hardware, the AMD Zen 4 series can compete against Intel’s current Alder Lake. However, AMD’s Zen 4 Ryzen 7000 coming out this month will have its hands full competing with the anticipated Raptor Lake from Intel due out later this month. You can read about the benchmarks here but the main takeaway is that average sales prices comes under pressure due to a more competitive market in PCs and gaming. This is in addition to the (15%) to (19.5%) decline in shipments noted below. Hence, we are seeing a much larger decline in revenue (40% decline in AMD’s revenue) driven by both lower ASP in addition to lower shipments.

A side note to consider is that the lower shipments are not only driven by slowing consumer demand. Some of this is coming from enterprise budgets, which raises questions around how fast enterprise budgets are deteriorating if AMD had little foresight into this issue.

I also want to note that enterprise can weigh on AMD’s data center segment even if Big Tech is a tailwind. If enterprises, government or education sectors are cutting server budgets than some effects from this will be seen on a YoY basis.

It can be tough to draw hard and fast conclusions is because comments about Q4 and next fiscal year are not available until November 1st. Operating from this unknown, I have the following information to share:

The most important question about the PC miss from AMD is when will we see a bottom?

There are two perspectives on this, the first is that Q3 is the bottom and the second perspective is that Q2 2023 will be the bottom. I imagine Q4 will be supported by the holidays to some extent, yet I would plan on lumpiness to continue in the Client category.

I also want to point out that estimates often have errors and this is true even for the very best analysts that go on record about PC sales. For example, IDC shows Apple shipments at 10 million for Q3 for 40% YoY growth whereas Gartner shows Apple’s global shipments at 5.8 million — this is a nearly 100% discrepancy between two reputable analyst firms.

IDC is reporting (16%) growth for the PC market in Q3 whereas Gartner is reporting (19.5%). The difference is primarily in the Apple numbers. Regardless, the PC market contracted 17% or 18% at the midpoint.

There are also institutional analysts who have numbers that are likely far off the mark: “September notebook shipments were up 12% month-over-month, well below Citi's expectation of up 23% due to continued inventory digestion and demand deterioration, Citi analyst Christopher Danely tells investors in a research note. As such, Q3 notebook shipments were up 3% quarter-over-quarter, well below the analyst's recently lowered forecast of up 7% quarter-over-quarter.”

This doesn’t matter for this quarter as AMD has provided the guidance but it is important to take into consideration for any upcoming forecasts.

Analysts who think Q3 will be the Bottom – Either PC Sales or Price:

Please note: the reason we look to institutional analysts particularly for a PC-related revenue miss is they have the ability to do channel checks and have well established contacts in the semiconductor industry given it’s a multi-decade market.

Jefferies analyst Mark Lipacis noted that AMD negatively preannounced Q3 revenues 17% below consensus, which is "entirely attributed" to client PC revenues being down 53% quarter-over-quarter and 40% year-over-year. Revenues for other segments, including datacenter, were in-line and he thinks Q3 revenues will "be near if not at the bottom," Lipacis tells investors. Given his belief that the stock's 39% underperformance versus the S&P 500 since November 30 discounts the bad news, he advises investors to be buyers of AMD and keeps a Buy rating with a $135 price target on the shares.

BofA analyst Vivek Arya lowered the firm's price target on AMD (AMD) to $90 from $100 and keeps a Buy rating on the shares after the company warned Q3 sales would be $5.6B, versus a prior $6.7B outlook. AMD did not update its Q4 outlook, but he expects trends to stay sluggish and models sales to decline further quarter-over-quarter on Client segment weakness, Arya said. He believes AMD's warning will have the most negative read-across for "PC peer" Intel (INTC), but also somewhat for Nvidia (NVDA) due to its exposure to consumer graphics and related memory and data center peers. He expects most semi cuts to be reflected when companies report Q3 results, which "could conceptually help create a trough in semi stocks," assuming the macro environment doesn't get worse, Arya added.

Bullet Points for a Bottom in/around Q2 2023:

Stifel analyst Ruben Roy lowered the firm's price target on AMD to $100 from $122 and keeps a Buy rating on the shares after the company announced preliminary Q3 revenue that was much lower-than-expected, driven by much lower-than-expected Client segment sales. He now forecasts Q3 non-GAAP EPS of 69c, versus a previous estimate of $1.06, but expects new product ramps and continued market share gains to drive a re-acceleration of revenue growth in the second half 2023, Roy said.

IDC stated that the PC and tablet market is forecast to decline (2.6%) before returning to growth in 2024. However, this Forbes editorial from a PC-veteran stated: “in my discussions with ODMs, the makers of PCs primarily in Taiwan and China, they suggested that for the enterprise market, we could see growth by as early as Q3 in 2023. In addition, they tell me that while consumer demand most likely will not rebound until 2024, they say they are now seeing increased orders for enterprise-based PC and laptops for delivery as early as Q3 of 2024.

PC vendors are getting comments from enterprise customers who held off buying large quantities of PCs and laptops during the last two years, and many of their PCs designated for IT use are well beyond their turnover date. With that in mind, some vendors are increasing their orders for IT-targeted PCs and laptops for delivery in late 2023.”

Northland analyst Gus Richard lowered the firm's price target on AMD to $80 from $105 and keeps an Outperform rating on the shares. Richard cut his 2023 estimates for AMD client revenue and data center revenue by $3.8B and $1.0B, respectively, due to the rapidly deteriorating PC market and declining enterprise spending following last week's pre-announcement.

My note: you can see the potential effects from enterprise budgets in Gus Richard’s estimates and this does include government and education budgets.

Note on the Other Segments:

We have 2024 tagged as the potential breakout year for automotive. If it happens sooner, we won’t complain. But, according to the product road maps that I’m tracking, 2024-2025 seems to be the sweet spot. We will table this for now but continue discussing in ongoing earnings coverage.

Regarding gaming, please note that AMD does not have exposure to crypto mining like Nvidia. On the flip side, Nvidia does not supply the CPU PC market like AMD. However, AMD does compete in the client-side GPU market which is reflected in the Client segment.

Conclusion:

Owning semis has been tough last few months, however, our research indicates Big Tech capex may be one of the strongest growth areas next year (again). We have to consider that outside of cybersecurity, there are going to be very few growth markets in tech in 2023, and of the growth markets we are tracking, very few will be in the double digits. What I’m referring to is an increase in budgets and/or spending in the areas that the tech participates in.

I like how Timothy Morgan from Next Platform worded the hybrid business model in his most recent write-up on AMD, and he also touches on Nvidia here:

“The numbers from both AMD and Nvidia demonstrate all too well how important it is to sell different kinds of compute engines across a wide variety of markets. While it is true that with such a hybrid approach there always seems to be something going wrong that limits profit and revenue growth, that hybrid vigor is nonetheless what helps a company make it through all of its hard times. IT suppliers who forget this – IBM and Hewlett Packard come immediately to mind – pare themselves down to a very tight market that makes them necessarily less resilient. And sometimes less relevant. Microsoft and Dell, and to a lesser extent Cisco Systems, seem to understand this.”

Our portfolio is centered on companies taking more market share from a competitor (AMD/Intel) or taking nearly all market share in one of the most expensive components of the data center (GPUs/NVDA). It’s not out of the question that the data center growth rates for AMD and NVDA outperform next year to help absorb the consumer-related weakness. Our best leading indicator for these stocks has been and will continue to be Big Tech capex.

Big Tech capex is important as it’s the one catalyst that can raise revenue estimates for next year, which subsequently raises bottom line estimates. Right now, the growth rate for AMD is quite abysmal at 3% for Q1 2023 so we want to see if we can get increased capex and then some revisions for H1 2023 (is the #1 thing I’m watching right now).

We will leave it to social media to stir up noise and distractions on the quality of Mark Zuckerberg’s avatar legs — and meanwhile, we will be busy digging up those capex numbers from Meta and other Big Tech companies. Last year, we got quite a bit of information regarding Big Tech capex from the October earnings reports, so you’ll be hearing from us if we get those updates in the next two weeks.

Marvell Q2 2023 Earnings & CXL Memory Catalyst

Posted on by io-fund

Marvell’s management team did an excellent job of acquiring Inphi and executing. Typically, we avoid M&A for a year to allow the financials to merge, yet in this case, leaning into the acquisition was a good choice.

The Marvell management team’s execution skills are needed once again because Marvell has an opportunity to greatly increase its revenue and profits if management can execute in a new market one more time. The opportunity is a new architecture called CXL that disaggregates memory from the CPU. CXL is attracting a lot of attention at industry events, such as Hot Chips 2022, because it’s focused on optimizing one of the most expensive parts of the data center – which is memory.

Before we go into the 2023-2024 Marvell product road map, and why it’s key to the company’s future, I want to discuss the fiscal Q2 2023 earnings.

Fiscal Q2 2023 Earnings Overview

The market is concerned over Marvell’s data center guidance of 20% growth next quarter. This is a slowdown from the most recent quarter at 48% YoY growth and earlier quarters at >100% growth.

Chart: Data Center YoY Growth

At an estimated $600 million, it will also mean a sequential decline both from Q2 and Q1, which were at $643M and $640M, respectively. Marvell stated it’s the on-premise business weighing on their cloud data center business and supply issues (more below).

Notably, Q2 of last year was an important moment for the company when 56% sequential data center growth grew from $277 million to $434 million in the span of three months following the close of the Inphi acquisition in April 2021. From there, the company has sustained Inphi’s already high growth levels for over a year.

The company is now at an annualized run rate of $6 billion, which the CEO reminded analysts, was the target for October of 2023. The company met the target originally provided at the October 2021 Investor Day one year earlier than expected. Notably, this was six months after Inphi was closed so M&A not a factor here.

Marvell’s Segment Overview:

  • The data center represents 42% of revenue at $643 million and grew 48% year-over-year.
  • The carrier infrastructure segment, which is wired and wireless and reflects 5G growth, reported 45% YoY to $285 million.
  • Enterprise networking grew handily at 53% YoY to $340 million and is expected to grow at 70% next quarter. We break this segment down below.
  • Consumer was down (1%) to $164 million and is expected to be down (10%) next quarter. Marvell has exposure to the storage market and this can weigh on the more robust segments.
  • Automotive was up 46% YoY to $84 million and is expected to be up 40% YoY next quarter. We also break down this segment below.

Marvell Financial Overview

Marvell was reporting negative top line revenue when we first covered it in 2019 and Marvell took another hit on revenue during Covid before accelerating to the 50%-74% revenue growth range.

The current quarter’s top line revenue in Q2 was at 41% which is a deceleration from Q1 with 74% revenue growth. The company guided for 29% year-over-year growth, which was a slight miss as analysts were expecting 30.3% growth in the fiscal Q3 quarter. The company reported EPS in line with adjusted EPS of $0.57. The guidance on EPS was a slight miss, however, at $0.59 reported versus $0.61 adjusted EPS estimated.

Semiconductors make a tougher investment as analysts can’t model too far into the future beyond what management teams provide. That is why there were many questions looking for help with how to factor in the “acceleration” in the data center the Marvell team is expecting in Q4 and what this will mean for CY2023.

An analyst asked if they can assume 10% QoQ in the data center for $1.7 billion overall revenue and the CEO said it sounded “a little on the high side.” This has led to analysts modeling $1.65 billion in revenue in Q4, for 22.5% growth. Therefore, despite a single-digit acceleration in the data center segment, there will still be a top line deceleration, if today’s forecast does not change.

The company’s margins and cash flow are a bright spot, and I believe this is being overlooked. If we get an acceleration in the data center into next year, then Marvell is fundamentally a much stronger company than it was during the previous data center streak.

On a GAAP basis, the gross margin was at 51% in the most recent quarter, up from 35% in the year ago quarter and up from 46% in FY2022. The company is guiding for the same GM of 51% next quarter.

The GAAP operating margin has improved quite a bit YoY to 8.3% in the current quarter compared to (25%) in the year ago quarter. This is also an improvement from Q1 with GAAP OM of 4.80%. The adjusted operating margin “hit a record” at 36.5% and is guided for 37% next quarter. Stock based compensation was at $139 million in the most recent quarter.

Cash flow is also improving with operating cash flow at $332 million, or 22% of revenue. This compares to $194 million last quarter and $819 million in FY2022. However, the company carries debt of $4.6 billion and has $617 million of cash on the balance sheet. This is a 1.8X net debt to EBITDA ratio.

Therefore, there has been substantial improvement yet Marvell does have a weaker debt profile than a company like AMD or Nvidia.

Chart: MRVL, AMD, NVDA Financial Debt to EBITDA (TTM)

Source: YCharts

Note on Supply:

Marvell is aligned with AMD in that they believe supply chain issues will ease in Q4 and into 2023. Here is what Marvell said in the opening remarks:

“Therefore, for our overall data center end market, we project revenue in the third quarter to decline sequentially in the mid-single digits on a percentage basis. However, we expect our data center revenue in the fourth quarter to increase on a sequential basis, anticipating an improvement in supply and new product ramps in cloud.”

Here is what AMD said:

“The visibility with our customers, especially our large cloud customers’ second half of this year into next year is very good. And we’re planning really for the next four to six quarters, and that gives us good visibility” and later provided many references toward supply coming online in Q4, such as: “But overall, the 7% increase [in gross margin], I think, is very well supported given all of the new product ramps that we have going on in addition to some additional supply that’s coming in as we get into the fourth quarter.”

It never hurts to have two management teams agree on the larger broad-based issue. However, since those reports, we’ve seen analysts cast doubts on the effects of macro for the rest of the year: “[Mizuho analyst Rakesh] checks show hyperscale orders are seeing "pushbacks" but no cancels, with Q3 trending flat quarter-over-quarter and Q4 "potentially soft." Rakesh lowered estimates for AMD "with macro headwinds clouding the near-term outlook."

Marvell’s Products:

In six brief years, Marvell has pivoted away from consumer (storage) products as the revenue mix was previously 62% consumer/38% infrastructure to being 11% consumer/89% infrastructure today.

This was driven partly by hyperscalers building data center infrastructure and AI/ML driving the need for faster data speed. Inphi also contributed to this.

Data Center Segment

PAM Solutions:

Marvell offers 200-gig and 400-gig PAM-based electro-optics — and the company recently added 800-gig solutions. This market sees tailwinds from the need for more bandwidth as the electro-optics connect short distances and long distances to increase data rates. PAM4 has replaced NRZ data transmission with the benefit of doubling the bit rate.

Hyperscalers are going through an upgrade cycle that requires high bandwidth and port density. PAM4 connects networking ASICs and machines, like servers and AI machines. Digital-based PAM4 uses analog-to-digital converters to clean up the signal in the digital domain before converting it back to analog to transmit.

Artificial intelligence and machine learning drives demand for the 800-gig PAM to increase the speed of input-output and to process the data flows. This doubles the throughput (bandwidth) due to an 8x100Gpbs optical transceiver for inside and between AI clusters.

In the fiscal Q1 results ending in April, management had stated: “our first quarter results benefited from a ramp in volume shipments of our 800-gig PAM solutions at two large customers.” The company has also stated that their products will see increase demand with the release of more powerful CPUs.

COLORZ 400:

COLORZ allows regional data centers to be linked together in the same metro region to function as one single mega data center. COLORZ silicon photonics technology allows data centers in the same metropolitan region to function like a mega data center through a “network fabric.” This facilitates faster edge computing within an 80/120 km distance for 30-megawatt data centers as they will be linked together and function like a 120-megawatt data center.

Per the most recent press release:

“As artificial intelligence (AI), machine learning (ML) and high-performance computing (HPC) applications continue to drive greater bandwidth requirements, cloud-optimized 400G solutions are needed to support high-speed data center interconnections. These requirements can only be met through high bandwidth connectivity offered in a small, cost-effective form factor.  The Marvell COLORZ II 400ZR enables cloud data centers the ability to increase the speed of data movement while keeping the power and cost low.”

Another press release stated the company shipped 100,000 units.

Here is what was said on the call about how/why the growth in the data center can continue:

Harlan Sur

Good afternoon. Thanks for taking my question. On the cloud optical connectivity business, this is both inside and between data centers, the upgrade cycles have been this really great multi-year tailwind for the team.

And if I look into next year, I believe that there's still at least one of the top four US hyperscale titans that's going to start the 400-gig PAM4 transition. You still have China CSPs that need to fire. You've got multiple customers on DR that's going to fire as well. Historically, like these transitions, I don't think have been impacted by a slowing macro demand environment. They're viewed as, I think, very strategic.

But is that how your cloud customers are thinking about these upgrades and your views on continued upgrade momentum in this segment for next year? And just relatedly, is the Innovium team on track to drive $150 million in revenues this year?

Matt Murphy

Hey. Thanks, Harlan. Yes, I think the first part of it, you got pretty well in terms of the transition on 200 and 400 gig PAM4 inside the data center. And then, the new ramps we're seeing in 400 gig ZR for DCI between data centers.

What I'd add on top of that is — which has been extremely strong and also, in some ways, a little bit of a constraint we've seen in terms of being able to keep up is, the demand on 800 gig, which is happening right now really around, obviously, very advanced AI workloads.

That is an area where, if we could obviously produce more material, we would be shipping it in Q3. So that's also a positive trend. So you've got sort of the transition going on all the way up to 800 gig, and that continues to look pretty good.

NOTE: Innovium is an acquisition that closed in 2021 and at time of acquisition was expected to add $150 million in revenue for CY2022/FY2023.

Compute Xpress Link (CXP): 2024-2025 Data Center Catalyst

Marvell is launching a new product line called CXL, which will improve how data centers add memory. Right now, a server must be opened to add DRAM and the DIMM slots are limited in number and don’t pass service history or bit-error history, which is needed by hyperscalers.

Memory pooling allows memory to scale independently from processors by taking memory for a task and then releasing the memory. The new fabric removes the need for local DRAM, which adds a bit of latency from 100ns to 140-160ns, however, there’s a possibility of adding a CXL accelerator to be more “cache coherent.”

The CXL switch will be used to accelerate protocol-level processing across ethernet, DPUs, SmartNICs and solid-state drive controllers (SSD).

What Marvell is proposing with CXL is a new server architecture to “dynamically assign memory resources between servers.” The result is boosted memory bandwidth and also the ability to enable memory pooling. The company sees a future where a new architecture will separate compute, memory and I/O racks with the interconnect being CXL. Partially-disaggregated racks are expected to deploy in 2024-2025.

Marvell is at the forefront of the shift toward “disaggregate memory from the CPU” because it currently supplies the optics that this new fabric will disrupt. Inphi is the leader in silicon optics, PAM-4, and the encoding of PAM-4 for PCIe 6.0.

2024 seems like a long ways off yet the market will be paying attention to this In Q2/Q3 2023.

Here’s an excerpt from the call:

“As you recall from our discussion last quarter, we see CXL as the next big evolution in cloud data centers that will enable us to increase our reach into the memory ecosystem and presents a multibillion-dollar SAM expansion opportunity for Marvell.

This includes a host of new products such as CXL expanders, cooling devices, switches and accelerators and the potential to embed CXL IP and a broad range of our data center products. Events and presentations at FMS strongly validated our excitement around CXL. This is the hottest topic at FMS with standing room-only presentations by many leading industry participants.

The Marvell booth, we demonstrated the industry's first CXL memory pooling solution, addressing the challenges related to memory scaling and cloud data centers. While the industry is still in the early stages of CXL adoption, we are working on closing significant opportunities right in front of us at key customers and envision a strong design win pipeline.”

Why Marvell for CXL?

There are a handful of companies going after the CXL opportunity. Marvell could be front runner as the company already works closely with memory OEMs by supplying HDD controllers, SSD controller and preamplifiers. The company also has an aggressive PCIe roadmap with the company shipping Gen 5 sockets whereas most SSD device are shipping Gen 4 solutions. Marvell is already investing in Gen 6, which in turn, attracts more Tier 1 memory OEMs.

Marvell acquired Tanzanite, a developer of advanced CXL technologies. The company plans to expand to CXL expanders, cooling devices, switches and accelerators.

The company has stated this will drive “a multibillion-dollar PAM expansion opportunity driven by CXL overtime.” (Note: Marvell is referring to PAM, their premiere product)

We will focus on this more next year. You can listen to a recent tech talk here on CXL. The presentation is located here. This is an article about Microsoft’s interest in CXL with a statement that “50% of their server costs are taken up by DRAM.”

Carrier Infrastructure:

The OCTEON processors and platform is an Arm-based compute architecture for embedded applications, such as wireless networking equipment including 5G, including switches, routers, firewalls and monitoring solutions.

The OCTEON DPU is used with SmartNICs and security accelerators with a 5nm design that delivers to the infrastructure industry the same processing node as consumer smart phones and high performance computing and shipped in 2021. The most recent release from last year was the OCTEON 10 DPU and Prestera carrier switches which combined consumes 50% less power than competitors (according to Marvell).

Marvell’s processors help 5G networks meet latency and bandwidth demand while also allowing the networks to upgrade as cellular standards evolve. Marvell also offers customized solutions, which is ideal for Tier 1 customers who can combine their IP with Marvell’s Arm v8 processors and accelerators.

Recently, Dell and Marvell partnered to develop a server-class accelerator card for 5G base stations based on Marvell’s arm-based OCTEON Fusion processor. The hardware accelerators deliver more processing power including processing solutions for smart radio heads to support massive MIMO antenna rays.

We wrote about MIMO a few years back in a reference guide: “Massive Multiple Input and Multiple Output (MIMO) sends the data through multiple data streams called layers, which increases parallelism and throughput. MIMO helps avoid lost signals with multipathing, which allows the base station to send multiple copies of the same signal for increased redundancy. 

Note: The antenna array is one fundamental change to 5G infrastructure. The initial 5G rollout will use existing cell towers, however, newer, dedicated 5G network infrastructures will require many more antennas than used in previous generations. Read more.”

The distributed unit (DU) shares the load with the radio unit by running L1 functions on the RAN protocol. Marvell has been a proponent of OpenRAN with the O-RAN platform, which is an open protocol and open platform that allows Marvell’s hardware to be used with various software vendors. Facebook (Meta) is a partner with Facebook Connectivity.

DPU processors, or digital processing units, are gaining traction for 5G transport, 5G RAN intelligent controllers, edge computing and cloud data center workloads. These hardware accelerators enable high speed connectivity and can improve packet processing rates by 5X. DPUs are ideal for power sensitive edge applications. Marvell’s strength in DPUs is one reason it may be able to stave off competition, which in the narrow field of 5G base stations includes Qualcomm/HPE and Analog Devices. Beyond 5G, Marvell has other competitors for DPUs such as AMD/Pensando and Nvidia.

Regarding 5G, over 7 million of the Octeon processors have been used in 3G, 4G and 5G base stations with Tier 1 customers. In the past, we reported that Samsung and Nokia use Marvell, and supplying these particular companies was a tailwind when Huawei was blacklisted. More recently, Marvell has stated they have design wins with four of the top five global OEMs and next-tier OEMs building base station equipment. These design wins are based on the 5nm platform.

Marvell uses TSMC for the 5nm OCTEON DPUs and this is an advantage because Marvell has the 5nm now and is able to move quickly on a 3nm release.

Notably, 5G has been a long time coming but I do believe it will reward investors over the next few years. Technavio has a CAGR of 67% for 5G equipment through 2025. The growth trend of 5G/edge computing is not one that we plan to complacent on as it will provide the next leg up for substantial capex spending similar to data center capex spending.

Enterprise Networking:

Marvell sells ethernet switches and ethernet PHYs to IT managers and networking equipment manufacturers. The company uses DSP technology for CAT5e ethernet cables to supply data rates up to 5Gbps with support for CAT6 and CAT6a.

Management discussed on the call that the main driver for this market right now is wireless, specifically WiFi 6 as the wireless rate line is now faster than the wired rate. The call also pointed toward content per port going up in the transition to multi-gig. According to the CEO, “it's not like 10%, 20%, 30%. It's sort of multiples on a per port basis of where it was before.”

Increased enterprise share and content gains from wired and wireless enterprise networking drove 53% YoY revenue growth and 19% QoQ revenue growth.

Automotive:

Similar to the networking that Marvell supplies enterprises and the data center, Marvell also supplies auto manufacturers with ethernet PHY transceivers, camera bridges and switches for in-vehicle networks. This is used for things like collision detection, lane warnings, and autonomous driving.

Marvell believes Ethernet will be the backbone for connected and autonomous vehicles to connect the electronic control unit (ECUs), cameras, sensors, and central compute devices. The Ethernet device is called Brightlane.

ON Semi has partnered with Marvell on use cases such as pairing a standardized protocol, such Ethernet PHY, with ON’s portfolio of ultra-dynamic range image sensors.

Automotive was up 46% to $84 million, yet was down 6% sequentially. Management cited supply issues rather than demand. Marvell counts eight of the largest 10 OEMs worldwide and 36 OEMs total. The company believes revenue growth will be 40% next quarter.

Note on Consumer Market:

Marvell sells hard disc drives (HDD) and solid state disc (SSD) controllers. This is a weaker segment, declining 1% YoY and 8% sequentially to $164 million. For next quarter, Marvell expects revenue to be down 10% YoY and flat sequentially.

Conclusion:

There is a new, powerful trend on the way that is on par with the cloud computing trend. This trend of edge computing will rely on distributed computing rather than centralized processing. Both will exist and rely upon each other but edge computing will have a stronger growth trend when it breaks ground (by virtue of being new/rapidly expanding). Much of this will be in sync with the 5G buildout.

Marvell has the potential to be a strong stock during this buildout as the company provides the base station hardware, supports MIMO antenna rays, beamforming, and accelerates 5G transport and controllers which results in high-speed connectivity.

The company also provides electro-optics and silicon photonics for increased data rates and a network fabric for edge computing. The edge is defined as many things, but what all definitions can agree on, is that the edge needs superior connectivity/networking. Electro-optics, silicon photonics, DPUs, SmartNICs and ethernet in the data center are a warmup for Marvell supplying edge servers and edge devices. As this occurs, the demand for Marvell’s product suite will increase.

In addition to this, Marvell is thinking outside the box by focusing on restructuring memory while most companies are focused on more powerful chips. CXL drives down costs on DRAM and is likely to rapidly adopted by hyperscalers once it becomes available. There’s no guarantee that Marvell will be the one to win the contracts but it’s certainly a front runner.

Nvidia Stock Is Ready To Rumble With RTX 40 Series And H100 GPUs

Posted on by io-fund

This article was originally published on Forbes on Sep 23, 2022,04:33pm EDTForbes on Sep 23, 2022,04:33pm EDT

Nvidia had a big week with GTC 2022 and management is clearly ready to rumble against any excess inventory from crypto mining. The negative catalyst from crypto mining and Nvidia's price action is eerily similar to Q4 2018/Q1 2019 —- yet the company is not the same company it was four years ago. This is apparent by Nvidia flexing some major product muscle by timing it's best-ever gaming release and it's best-ever AI chip to hit the market in October.

We draw important parallels (pun intended) between the last crypto mining selloff and this selloff with key reasons as to why this time the stock's comeback will be quicker.

Nvidia stock has been in the clutches of a steep drawdown after the company has faced nearly every headwind imaginable: United States-China tensions, supply chain disruptions spanning many components, tough comps on the data center, tough comps on gaming, and a less-than-rosy macro environment.

The most impactful headwind, however, was Ethereum’s merge to Proof of Stake (PoS), which ultimately lowers demand for gaming GPUs. This contributed to a $2.5 billion cumulative miss in revenue driven by the gaming segment.

Nvidia’s stock performance in 2018 and 2022 feels eerily similar as the stock sold off 54% in 2018 specifically because of a gaming miss tied to crypto mining. Today, Nvidia is currently 57% YTD.

It took eighteen months for Nvidia to recover its all-time high from the Q4 2018 selloff (Sept 2018 through Feb 2020). Despite the uncanny similarity that 2018 and 2022 may have — Nvidia is actually a much stronger company today than it was four years ago.

Below, we discuss a few key reasons Nvidia stock will recover quicker this time around.

Drilling into Parallels Around the Gaming Miss

During the Q3 2018 results released in November 2018, Nvidia gave Q4 2018 revenue guidance of $2.7 billion, below the analysts’ consensus estimate of $3.4 billion. In January 2019, the company again lowered revenue guidance from $2.7 billion to $2.20 billion, which suggests a total revenue miss of $1.2 billion. Gaming revenue in Q3 2018 was $1.76 billion, up 13% YoY and down 2% QoQ. In Q4 2018, gaming revenue was $954 million, down 45% YoY and down 46% QoQ.

In the most recent quarter ending July 2022, the company missed on gaming with revenue of $2.04 billion, which is 33% lower than the year ago quarter and 44% lower sequentially. The company is expecting a further decline in gaming sequentially for Q3. According to one analyst on the call, they are modeling for a further 30% sequential decline in gaming and professional visualization offset by low to mid-single digit growth in data center and automotive. The CFO affirmed this understanding is correct.

After 2018, although it took Nvidia eighteen months to reclaim its all-time highs, in 2020-2021, Nvidia would go on to stage a remarkable turnaround as the stock led tech mega cap stocks in gains. This was not simply because all tech performed well during those years – if you compare Nvidia to Meta, Amazon and Google, you’ll see something unique occurred with Nvidia that caused the stock to outpace its peers. In all cases except Apple, Nvidia doubled, tripled or quadrupled the performance of other mega cap stocks.

Chart Nvidia leading over all mega cap stocks

Source: YCHARTS

Perhaps most impressive, Nvidia is still in the lead over all mega cap stocks despite a 57% drawdown this year. It’s the company’s past performance that makes it well worth the time to answer: can Nvidia do it again?

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Nvidia’s GeForce RTX 40 Series is Perfectly Timed

Next quarter, Nvidia was expected to report $6.92 billion and the company guided for $5.9 billion. This is down from $7.10 billion in Q3 of last year. This will be a 17% decline in revenue. Due to this, analysts expect Nvidia to end fiscal year 2023 with 0.8% revenue growth, or $27.13 billion in total revenue.

It’s not only the top line valuation that is affected by this cut in guidance but it’s the bottom line, as well. In previous quarters, high average sales prices drove $2 billion to $3 billion in operating profits and net profits, whereas in the most recent quarter, the company is reporting $500 million and $656 million, respectively.

The GAAP EPS reported was $0.26 compared to $0.94 in the year ago quarter. Adjusted EPS was $0.51 versus $1.04 for the year ago quarter.

Although it’s tempting to redirect the conversation toward higher-growth segments, the $2.5 billion total miss between two quarters came from gaming and it’s prudent for investors to start here (for now) when analyzing the stock for a potential recovery.

The company stated the miss was driven by both lower units and lower average sales prices including reduced consumer demand. The company is not commenting on crypto as they state they have no visibility here as to how the GPUs are being used, however, it’s certainly contributing to the bulk of this decline.

Notably, AMD reported gaming growth of 32% to $1.7 billion which provides a better picture of reduced gaming demand minus crypto. Nvidia believes some of their weakness is also from preparation for a new product generation that will be announced this month.

Per the earnings call, there are two ways that Nvidia plans to overcome the crypto mining selloff which could produce a faster rebound than 2018.

First, Nvidia is restricting supply on its current gaming model. Per the CFO: “Across those two quarters, the Q2 of ‘23, the Q3 of ‘23, we have likely undershipped gaming to our end demand significantly.”

Following the call, we estimated for our premium members that the amount undershipped is a minimum of $1 billion. The reason behind this is to help keep prices stable and to increase demand for the RTX 40 Series.

Second, Nvidia announced its GeForce RTX 40 Series at the GTC 2022 Conference this week.

The new Ada Lovelace architecture which uses 76 billion transistors and a 4nm production process. In the keynote, the CEO stated: “Nvidia engineers worked closely with TSMC to create the 4N process optimized for GPUs. This process let us integrate 76 billion transistors and over 18,000 CUDA cores, 70% more than the Ampere generation.”

The improvement from 8nm to 4nm means more transistors on the GPU, which results in better performance as the 4nm processes data faster.

In the gaming world, this much anticipated release is expected to be 2-4X faster than the RTX 3090 Ti. The flagship AD102 GPU model will have 144 individual streaming multiprocessors (SMs) in one die compared to 84 SMs in the Ampere architecture. As stated, the AD102 will also have a 70% increase in CUDA cores over the RTX 3090 Ti.

In addition to this, Nvidia is releasing a new feature called Shader Execution Reordering (SER) which will improve ray-tracing performance by 3X with 25% faster frame rates. Rather than deliver workloads sequentially, the GPUs are able to reorder the workloads to process more workloads at once which results in more power and better performance.

Deep learning super sampling (DLSS) refers to using AI to predict the next pixel. The new DLSS 3.0 not only predicts pixels but will also use AI to predict frames. This results in “up to four times” better performance over traditional rendering.

The first release date for the RTX4090 models is October 12th with a starting price of $1,599. There is a second release date in November for the RTX4080 models with prices of $1,199 and $899. Notably, mid-range RTX 40 series will outperform the previous generation’s high end models. This is due to the Ada Lovelace architecture which offers 1,400 Tensor TFLOPs versus 320 Tensor TFLOPs which means the DLSS is superior and the high-end RTX 30 Series cannot compete with the mid range RTX 40 series.

The popularity of this release will help determine if Nvidia can stage a comeback in the gaming segment. Here is what analysts are saying:

“Morgan Stanley analyst Joseph Moore said his "biggest takeaway" from the keynote at Nvidia's GTC conference were the higher prices of gaming GPUs, which increases his conviction about the pace of gaming revenue recovery next year. Prices that are 28% higher than the baseline price from two years ago for the higher volume 4080 should drive material growth in revenue, said Moore, who sees revenues in the gaming segment rebounding from the current quarter run rate of $5.5B or so to $9.5B next year.”

“Given the channel inventory work downs in the July and October quarters, the products should be "strong demand catalysts" into 2023, Harlan Sur of Chase tells investors in a research note.”

Nvidia Continues to Build a GPU Moat with H100

In 2018, we stated in our free newsletter that Nvidia had built a moat in the GPU-powered data center. This was a bold statement as the company would go on to have negative year-over-year data center revenue in 2019. Yet, fast-forward and it’s quite clear that Nvidia is unshakeable in this segment, which has surpassed gaming as Nvidia’s most valuable segment.

I’ve written quite a bit about Nvidia, which you can reference here and also here. However, I will keep it simple by saying the A100 GPU is what led the company’s gains since Q2 2020 (detailed here) and the Hopper H100 GPU is what will lead the company’s gains for the next two yearsdetailed here) and the Hopper H100 GPU is what will lead the company’s gains for the next two years.

In the most recent quarter, data center revenue of 61% is down from 83% last quarter yet accelerated YoY from 35% growth in the year ago quarter. The earnings call reviewed some of the challenges Nvidia faced in the quarter that led to the 1% sequential growth.

First, Chinese hyperscalers slowed their infrastructure investment this year yet the slowdown is unlikely to last much longer. Due to being a large market for Nvidia, the data center growth was impacted by this. The reason Nvidia was able to meet expectations is because “North America doubled year-over-year in revenues.” As of now, supplying the Chinese military is restricted for Nvidia, but this does not include supplying the hyperscalers.

Second, demand continues to outstrip supply yet there are many components to Nvidia’s systems and they are experiencing supply chain issues.

“We were challenged this quarter with a fair amount of supply chain challenges because as you know, we don’t just sell the GPU chip, but these systems are really complex with a large number of chips in the system components that we offer like HGX […] all of the components that have to come together for us to be able to deliver the final component.”

H100 Hopper Coming in October

On the earnings call, an analyst asked if the company expects data center growth to re-accelerate when Hopper ships: “Do you think that Hopper, as that comes fully available, it sounds like in fiscal 4Q, that you actually see Data Center growth reaccelerate as that product cycle materializes.”

The CFO Kress stated: “Our Data Center yes, we do expect it to grow. It may grow about what we just saw between Q1 and Q2. We’ll continue to look at it.”

I believe this means the data center will accelerate above 61% but not to exceed the 83% from Q1. Ultimately, the CFO may not have full visibility into Hopper sales until the units ship and are tested by customers, who in turn, often buy more if the product exceeds expectations.

On that note, the new 4nm chips are bound to impress. The H100 GPUs and the DGX H100 server pods and super pods offer Nvidia the next leg-up as the company has solved an important bandwidth issue.

Hopper tackles some of the bigger issues around previous generations like speeding up algorithms by offering dynamic programming on GPUs to break down problems to simpler subproblems. The new GPUs also boost bandwidth by 3X with SHARP in-networking computing and Infiniband Switches, and the H100 can leverage NVLink to connect eight H100s into one giant GPU for 640 billion transistors, 32 petaflops, 640GB of HBM3, and 24 terabytes per second of memory bandwidth.

The H100 has about 50% more memory and interface bandwidth than the A100. That’s 1.5X more bandwidth with the NVLink connection and PCIe 5.0 doubling the bandwidth of PCIe 4.0. The H100 will ship with support for 80GB of HBM3 memory at 3 TB/s speed

Where the H100 really stands apart is the leap in performance with about 3X more performance than the A100 and the H100 is up to 6X faster. The A100 lacked support for FP8 compute at default whereas the H100 will leverage a transformer engine to switch between FP8 and FP16, depending on the workload.

According to Nvidia, the H100 delivers 9X more throughput in AI training, and 16X to 30X more inference performance. The company also states in HPC application-specific workloads, the H100 is 7X faster. The goal of the H100 was not only to add more transistors and make the H100 faster, but to also offer function-specific optimizations. This is achieved through the transformer engine.

Last week, MLPerf published artificial intelligence performance tests. The parent company MLCommons provides the industry standard for benchmarking deep learning, AI training, AI inference and HPC. The H100 Tensor Core GPUs delivered 4.5X more performance than the A100 in offline scenarios and 3.9X more in the server scenario compared to its predecessor the A100.

The Hopper H100 GPUs are in full production and availability starts next month and will have over 50 server models by the end of the year and “dozens more in the first half of 2023.”

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Nvidia’s Automotive Opportunity is Massive

Nvidia’s lead in automotive across dozens of OEMs requires its own analysis, which we will write for our free newsletter subscribers next year. Hyperion 8 is shipping in 2024 and Hyperion 9 will ship in 2026. However, as long-term Nvidia investors, now is a good opportunity to remind my readers of the long-term vision for yet another large and sweeping revenue segment.

Although a small segment today of only $220 million, automotive grew 59% sequentially and 45% year-over-year. The company has a $11 billion automotive design win pipeline.

At GTC this week, Nvidia announced a new superchip named “Thor” which will deliver 2,000 teraflops of performance, up from 200 teraflops from the current generation “Orin.” The chip has a transformer engine which can process video data as a single perception frame and offers 8-bit floating point (FP8) precision to avoid task loss when converting model data from one platform to another platform.

More on the Omniverse

We’ve covered the Omniverse platform in the past including an interview with Nvidia’s Richard Kerris you can view here.

At GTC this week, Nvidia launched Omniverse Cloud, which is a infrastructure-as-a-service software offering to reduce the complexity around building 3D virtual worlds and assets. This removes the need for local compute power and opens up the ability for more creators to access 3D world creation.

Regarding the China Restrictions

The United States government is restricting sales of high-performance chips to China as Nvidia’s AI chips could be used for military purposes. A spokesperson for Nvidia stated the products where the new licensing requirement applies is the A100, H100 and systems that include DGX.

The restrictions apply to Russia yet Nvidia has stated there is no exposure to Russia for their products. In a recent SEC filing, the company stated: The Company’s outlook for its third fiscal quarter provided on August 24, 2022 included approximately $400 million in potential sales to China which may be subject to the new license requirement if customers do not want to purchase the Company’s alternative product offerings or if the USG does not grant licenses in a timely manner or denies licenses to significant customers.

At this time, Nvidia has applied for an exemption and there has also been a clarification that Nvidia can continue to develop the H100 in China through September 1, 2023 through the company’s Hong Kong facility.

Per the SEC Filing dated August 31, 2022:

The U.S. government has authorized exports, reexports, and in-country transfers needed to continue NVIDIA Corporation’s, or the Company’s, development of H100 integrated circuits after the Company filed its Current Report on Form 8-K with the U.S. Securities and Exchange Commission on August 31, 2022. The authorization also allows the Company to perform exports needed to provide support for U.S. customers of A100 through March 1, 2023. Additionally, the U.S. government authorized A100 and H100 order fulfillment and logistics through the Company’s Hong Kong facility through September 1, 2023.

Some analysts have stated that being granted an exemption is “feasible.” Mark Lipacis of Jefferies is modeling for a $200 million hit to October rather than the $400 million identified risk. Harlan Sur of JP Morgan noted AMD is working on getting export licenses for its customers and helping them transition to products that fall below the performance threshold to help mitigate the downside risk.

According to a new report, Nvidia has asked TSMC to rush high-end GPU orders before the US sanctions begin. The report says that TSMC has a special program to speed delivery of orders at a higher negotiated price and can help to cut the delivery time in half. This could lead to a surprise bump in Q4 revenue for the company.

Conclusion

Nvidia is not the same company that it was four years ago. In 2018, Nvidia was a gaming company with promising AI tailwinds. Today, Nvidia’s AI products serve nearly every enterprise company’s artificial intelligence and machine learning ambitions.

The company has an impressive launch schedule starting in October for two flagship products – the RTX 40 Series and the H100 GPU. The timing of these releases is no coincidence as it’s a rapid two months following the crypto/gaming revenue miss. Suffice to say, Nvidia’s management team is prepared to rumble —- putting its very best release in gaming and its most powerful AI chip to-date up against the crypto mining selloff. If history is any indication, the turnaround will only be a matter of time.

Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in NVDA at the time of writing and may own stocks pictured in the charts.

Nvidia Q2 Earnings: Gaming Weighs on The Real Thesis

Posted on by io-fund

The Hopper architecture is ramping and it’s yet again going to disrupt the GPU and AI accelerator market. I’ve written quite a bit about Nvidia, which you can reference here. However, I will keep it simple by saying the A100 GPU is what led the company’s gains since Q2 2020 (detailed here) and the Hopper H100 GPU is what will lead the company’s gains for the next two years.

But first, we have to get over the gaming hump. This has singlehandedly taken Nvidia’s revenue down to +3% growth this quarter with Nvidia expected to report $8.2 billion in revenue which came in at $6.7 billion.

For next quarter, Nvidia was expected to report $6.92 billion and the company guided for $5.9 billion. This is down from $7.10 billion in Q3 of last year. This will be a 17% decline in revenue. The company is expected to end fiscal year 2023 with 1.2% revenue growth, or $27.24 billion in total revenue.

It’s not only the top line valuation that is affected by this cut in guidance but it’s the bottom line even more so. In previous quarters, high average sales prices drove $2 billion to $3 billion in operating profits and net profits, whereas in the most recent quarter, the company is reporting $500 million and $656 million, respectively. The GAAP EPS reported was $0.26 compared to $0.94 in the year ago quarter. Adjusted EPS was $0.51 versus $1.04 for the year ago quarter.

Data center revenue of 61% decelerated sequentially down from 83% last quarter yet accelerated YoY from 35% growth in the year ago quarter. Gaming revenue fell 33% YoY whereas it had grown 31% YoY in the previous quarter. Professional Visualization also fell 4% whereas it had grown 67% in the previous quarter and had 100% growth in previous quarters, as well. Automotive was up 45% and along with data center helped to absorb the fall-off from Gaming and ProViz.

Gaming Hump: How Long Will It Last?

The company missed on gaming with revenue of $2.04 billion, which is 33% lower than the year ago quarter and 44% lower sequentially. The company is expecting a further decline in gaming sequentially for Q3. According to one analyst on the call, they are modeling for a further 30% sequential decline in gaming and professional visualization offset by low to mid-single digit growth in data center and automotive. The CFO affirmed this understanding is correct.

This is driven by both lower units and lower average sales prices including reduced consumer demand. The company is not commenting on crypto as they state they have no visibility here as to how the GPUs are being used, however, it’s certainly contributing to the bulk of this decline.

Notably, AMD reported gaming growth of 32% to $1.7 billion which provides a better picture of reduced gaming demand. Nvidia believes some of their weakness is also from preparation for a new product generation that will be announced next month.

Here was the first question on the call:

C.J. MuseC.J. Muse

I think the question we all have is what is normalized revenues for gaming for you guys? Obviously, this is a challenge to you as well. But curious how you’re thinking about it today. Is the fiscal ‘20 recovery post the first half ‘19 correction an appropriate framework, or was that inflated by crypto as well? And I guess, as part of that, how do we think about the cascading in of the new product cycle? And is there potential for future reserves needed to be taken if gaming does not meet your new updated outlook? Thanks so much.I think the question we all have is what is normalized revenues for gaming for you guys? Obviously, this is a challenge to you as well. But curious how you’re thinking about it today. Is the fiscal ‘20 recovery post the first half ‘19 correction an appropriate framework, or was that inflated by crypto as well? And I guess, as part of that, how do we think about the cascading in of the new product cycle? And is there potential for future reserves needed to be taken if gaming does not meet your new updated outlook? Thanks so much.

Management avoided the crypto question and instead answered the following:

The CFO Collette Kress stated: “Across those two quarters, the Q2 of ‘23, the Q3 of ‘23, we have likely undershipped gaming to our end demand significantly. We expect that sell-through or essentially our end demand for those combined two quarters of Q2 and Q3 to be approximately $5 billion […].”We expect that sell-through or essentially our end demand for those combined two quarters of Q2 and Q3 to be approximately $5 billion […].”

She is referring to about $1 billion being under shipped (or reduced sell-in) if we assume flat growth for gaming next quarter as the company attempts to rebalance inventory. It would be even more of an under shipment if gaming does decline sequentially.

Note: the next-generation GeForce RTX 40 Series the company is referring to is to be announced in September at GTC 2022.

The CEO Jensen Huang stated: “Our strategy is to reduce the sell-in — reduce the sell-in this quarter, next quarter to let channel inventory correct. Obviously, we’re off the highs, and the macro condition turned sharply worse. And so, our first strategy is to reduce sell-in in the next couple of quarters to correct channel inventory. We’ve also instituted programs to price position our current products to prepare for next-generation products.”And so, our first strategy is to reduce sell-in in the next couple of quarters to correct channel inventory. We’ve also instituted programs to price position our current products to prepare for next-generation products.”

The next question was similar and also about gaming, which the CEO responded again that they are rebalancing the supply and demand by reducing the sell-in (or essentially limiting the supply side).

“We believe that by the end of the year, we’ll be in a good shape going into next year. And so, I hope that answers your question. But, the important thing is our sell-in rate is far below what is happening in the market for sell-throughs. The sell-through is solid, has increased 70% since pre-COVID. And so, the gaming market is really quite vibrant.”We believe that by the end of the year, we’ll be in a good shape going into next year. And so, I hope that answers your question. But, the important thing is our sell-in rate is far below what is happening in the market for sell-throughs. The sell-through is solid, has increased 70% since pre-COVID. And so, the gaming market is really quite vibrant.”

My takeaway is that we have two more quarters before gaming rebalances. Management said this again toward the end of the call: “Still, the fundamentals of gaming are strong. We’ll get through this over the next few months and go into next year with our new architecture.” Nvidia states their gaming GPUs command the Top 15 list for Steam with 1,350 titles and there are 20 million registered GeForce NOW members.

Data Center Has More Runway

The information on the call about the data center was especially interesting because the company met expectations at 61% growth yet saw many challenges in the quarter. The challenges resulted in 1% sequential growth. As detailed below, revenue from North American hyperscalers doubled revenue year-over-year and it was Chinese hyperscalers that weighed on growth.

Demand continues to outstrip supply yet there are many components to Nvidia’s systems and they are experiencing supply chain issues.

“We were challenged this quarter with a fair amount of supply chain challenges because as you know, we don’t just sell the GPU chip, but these systems are really complex with a large number of chips in the system components that we offer like HGX […] all of the components that have to come together for us to be able to deliver the final component.

And then furthermore, these data centers sit idle until the last piece comes together. And the last piece includes very complicated switches and very complicated NICs and networkings and cables. And so these — building these high-performance computing data centers at very large scale for the world’s cloud is not particularly easy. And so the supply chain challenges have been somewhat disruptive. But the demand is there.”

The CFO elaborated by saying: “Some of our supply arrived very late in the quarter. We had very little time from a logistics and availability to get those things out. Customers were impacted as well by availability of key third-party other components that we weren’t offering, which were slowing down some of their deployments. So what we did in our Q2 orders that couldn’t be delivered in Q3, given that some of these supply constraints existed, and we had Q3 demand where we did have supply in Q2.”

Management also discussed how Chinese hyperscalers slowed their infrastructure investment this year and how this slowdown can’t last forever. Due to being a large market for Nvidia, the data center growth was impacted by this. The reason Nvidia was able to meet expectations is because “North America doubled year-over-year in revenues.”

We’ve discussed in detail the Hopper H100 GPUs and the DGX and HGX systems, as well as the Grace CPUs, which you can reference here. According to management “With respect to Hopper, we’re in full production now. And we’re racing to get Hopper 2, all of the CSPs are dying to get them […] We expect to ship substantial Hoppers in Q4.”

An analyst snuck a question in asking if the company expects data center growth to re-accelerate when Hopper ships: “Do you think that Hopper, as that comes fully available, it sounds like in fiscal 4Q, that you actually see Data Center growth reaccelerate as that product cycle materializes.”

The CFO Kress stated: “Our Data Center yes, we do expect it to grow. It may grow about what we just saw between Q1 and Q2. We’ll continue to look at it.”

My note: the data center was at 83% growth for Q1 and 61% growth in Q2.

The CEO Huang stated: “The first thing I’d say, Aaron, is that we are selling in or we’re selling far below the market demand, far — excuse me, far below the market sell-through. And the reason for that is to allow the inventory the channel inventory, the OEM inventories to correct. And this allows us to prepare for our next generation. And our next generation has Hopper for compute, but we also have the next generation for computer graphics that will be coming to market.”

The takeaway is that the data center is very likely to re-accelerate from Hopper.

Transformers

Since our new thesis published in July discussed the importance of transformers, I wanted to pull out some comments on the call as it was the primary growth driver the CEO discussed. Notably, he discussed it many times in an effort to explain the importance of transformers to the company’s strategy moving forward.

“And then, of course, over the last several years, a very important model has emerged called transformers. You and I’ve spoken about this model several times in the past. And it’s been found that this transformer model, this large language — this language model, which when scaled up in size, exhibits really spectacular and effective capabilities for — to be used to learn skills with either few shots or almost no shot, meaning it could learn skills, it could perform skills that it has never learned because the knowledge was somehow encoded from the large amount of data that it had learned from.”it could perform skills that it has never learned because the knowledge was somehow encoded from the large amount of data that it had learned from.”

The CEO elaborated again on Transformers when he was asked about whether Hopper can help re-accelerate the company’s data center revenue:

“Hopper is a giant new generation because it is designed to perform this new type of AI model called Transformers. It has an engine inside it called Transformer engine with numerical formats and pipelines that allows us to do a spectacular job on Transformer-type of models, which includes large language models, but it also includes computer vision models that are now able to be processed with this new type of AI model called Transformers.Hopper is a giant new generation because it is designed to perform this new type of AI model called Transformers. It has an engine inside it called Transformer engine with numerical formats and pipelines that allows us to do a spectacular job on Transformer-type of models, which includes large language models, but it also includes computer vision models that are now able to be processed with this new type of AI model called Transformers.

And so I fully expect Hopper 2 to be the next springboard for future growth. And — and the importance of this new model, Transformer, can’t possibly be understated and can’t be overstated. This is the impact of this model across robotics, computer vision, languages, biology, chemistry, drug design is just really quite spectacular. And I’m sure that you’ve been hearing about this new breakthrough in AI, and Hopper was designed for this.”And so I fully expect Hopper 2 to be the next springboard for future growth. And — and the importance of this new model, Transformer, can’t possibly be understated and can’t be overstated. This is the impact of this model across robotics, computer vision, languages, biology, chemistry, drug design is just really quite spectacular. And I’m sure that you’ve been hearing about this new breakthrough in AI, and Hopper was designed for this.”

And, there were more comments which I’m inclined to continue quoting because I think this company is doing very important things that are being overlooked by the gaming miss. So, bear with me as I provide yet another quote:

“Hopper was designed for transformers. The new transformers was going to be important. Nobody could have predicted the profound importance of large language models […] And to have AI that was never trained on a particular skill and yet within 1 shot or 1 shot of trying or even no shots, are able to perform that skill is beyond anybody’s expectations, I would think. And so I think the — the success of Hopper is — reflects the amount of work and pent-up demand for large training systems that Hopper is going to go into. If that’s an indicator, I think Hopper is going to be a spectacular success.”And so I think the — the success of Hopper is — reflects the amount of work and pent-up demand for large training systems that Hopper is going to go into. If that’s an indicator, I think Hopper is going to be a spectacular success.”

Automotive

The word “inflection” was used for automotive. Although a small segment of only $220 million, it grew 59% sequentially and 45% year-over-year. The company has a $11 billion automotive design win pipeline. This segment was the focus of a recent deep dive so I’ll keep it simple for now and just say there are promising things happening here and this may have been the first quarter of many where we see automotive continue to grow quickly.

Professional Visualization

Professional Visualization was a blemish this quarter and is expected to decline sequentially next quarter. Of the 30% sequential decline expected in Q3 in gaming and professional visualization, one-fourth will come from this segment and three-fourths from the gaming segment.

Analysts were poking around to see if this means enterprise spending is weaker than anticipated but I believe it simply means the Omniverse is discretionary compared to automotive and data center (which are industries that are very competitive at the moment).

Conclusion:

We have a high conviction company taking a breather on growth and each investor should approach this in a way that’s best for them. Some will decide to hold and ignore the noise, and others will want to re-allocate for the next quarter to a stronger company fundamentally in CY2022. There could be signs of a stock bottoming but this is different than a stock rallying. How I/O Fund handles this is subjectively up to us, but we will of course disclose our trades in the event they are useful.

I’ve given Knox the green light to trim from Nvidia 2-3% and add this to AMD, which I believe is a bit fundamentally stronger right now. AMD doesn’t have a gaming hump to get over and I don’t have a strong feeling if one has the leading allocation over the other for a period of time so we will see how we adjust here as they are both high conviction. Certainly, Nvidia is a high valuation, and considering the time-out the company is going to be taking for a quarter or two on revenue growth, we have to be realistic on what the stock price will be capable of compared to its peer AMD. Those are my thoughts fundamentally, which I covered here for AMD, but we will use technicals to guide this, as well. I wrote something similar in a brief note on Wednesday night.

Anything we trim from Nvidia now will be added back to take full advantage of the Hopper-inspired data center growth and the automotive (positive) surprises we have in store over the next few years.

Note: Information above has been updated 08/27 to reflect new analyst expectations for fiscal year 2023 of 1.2% revenue growth, or $27.2 billion.

Quick Update on Snowflake and Nvidia

Posted on by io-fund

Snowflake exceeded top line expectations, as outlined on our forum here. Management focuses on product revenue versus total revenue and GAAP metrics are a bit buried under the more accessible adjusted non-GAAP metrics. However, Snowflake delivered what the market needed to see – which was 83% product revenue growth compared to 72% growth expected. The guide for Q3 was in line with expectations while FY2023 was slightly raised from $1.893B at the midpoint for 66% growth to $1.910B at the midpoint for 67.5% growth.

The top key metric to note was a re-acceleration in customers with TTM product revenue above $1 million, which was at 112% this quarter at 246 customers, up from 98% growth last quarter. This is an important forward-looking metric as it takes 9 months to fully onboard new customers. Another reason this key metric holds more weight is the consumption model means the upside is uncapped, whereas with SaaS, the monthly amount has a ceiling (usually). You can read more about the consumption model here.

Net revenue retention rate is down 300 basis points sequentially but it up 200 basis points year-over-year and this re-acceleration is what’s important. RPO growth of 78% is the lowest in reporting history, down from 82% last quarter and 122% in the year ago quarter.

Analysts did note on the call that there are tougher comps for RPO coming down the line in Q4 (quarter ending in Jan 2023). It was in this quarter that RPO moved from $1.8 billion to $2.6 billion. Right now, it stands at $2.7 billion, so not too much H1 growth over the past six months. This is simply something to note. Of this RPO, 57% is recognized over the next 12 months, or $1.5 billion.

Here is what was discussed:

Brad RebackBrad Reback

Hi, thanks very much. Mike, I know you mentioned the 3Q consumption comp. You also have a really, really difficult 4Q RPO comp. But given your commentary, should we expect a healthy end to the year given that renewal pool? Thanks.

Mike ScarpelliMike Scarpelli

Yes. We expect we will have a big increase in RPO. But I’m not guiding to it. You’ll have to wait and see. I’m never going to guide RPO.

Total customer growth was at 36% growth compared to 40% growth last quarter. As noted above, it’s the TTM > $1M that matters.

There is no denying that on a GAAP basis, Snowflake is largely unprofitable. The company’s GAAP operating margin was at (42%) compared to the adjusted operating margin of 2%. The operating losses of $207 million this quarter increased from $189 million in GAAP operating losses last quarter. Stock based compensation increased from $164 million in the year ago quarter to $209 million in Q2 2022.

Free cash flow fluctuates with $54 million in free cash flow this quarter. This is up from ($12) million in free cash flow last year. The free cash flow margin is 11% and the company raised its adjusted free cash flow guide for the year from 15% of revenue to 17% of revenue. The company has $5 billion on the balance sheet.

Moving Forward …

The top catalyst for Snowflake (in my opinion, and was discussed on the call) is Snowpark going into production with Python. It’s not open to general availability yet.

Here is what was discussed on the call:

Frank SlootmanFrank Slootman

Yes, I will start, and maybe Christian can finish. Python is – so Snowflake for Python is red hot, and people are jumping that for us to declare it GA, which is something and we have customers that are really wanting us to let them use it in production now some of the largest customers that we have. So, pressure is on because the demand is there. The thing about the Iceberg Open Table formats that really completely open Snowflake up to be – for Snowflake cables to be used by anybody and everybody that can support that format. We are seeing incredible results in terms of performance of like executing against that file format. So, these are all very, very, very promising developments for us. And I think that the pressure is on for us to declare these things generally available because people are trying to rip them out of our hands right now.

Mike ScarpelliMike Scarpelli

Yes. As we said at our Summit conference, we expect those to be GA at the end of this year. So, a meaningful contribution to consumption will happen next year.we expect those to be GA at the end of this year. So, a meaningful contribution to consumption will happen next year.

Here is what we’ve said in the past:

“Snowpark offers the ability to migrate business logic with popular programming languages Python, Scala/Java Virtual Machine or Java. The library and DataFrame API allow querying and processing data without having to move data to where the application code runs. This extends programming functionality for ML model training and allows data processing to run natively in the data cloud. 

Prior to Snowpark, code deployment required separate infrastructure. Building applications that interact with Snowflake’s virtual warehouses minimizes processing time and lowers the learning curve/broadens adoption of complex data pipelines by removing the need to move or copy data into other systems to overcome working with SQL.

The recent announcement of adding Snowpark for Python is key because of Python’s widespread popularity among developers. With the Snowpark Accelerator, Snowflake is courting developers to build more applications and this is likely to help Snowflake maintain a competitive advantage with a newer class of machine learning startups.”

Nvidia …

Nvidia remains one of our highest convictions and we’ve laid out those reasons in great detail. We will provide an earnings overview soon but you’ve likely already heard through the pre-announcement that gaming was down 44% sequentially. The company’s guidance also missed by $1 billion with $5.9 billion guided versus $6.9 billion expected.

Nvidia is undeniably the highest priced semiconductor, as well, and the one issue investors face when a company misses on EPS (noted in the pre-announcement) is the valuation gets richer overnight. Nvidia has been trading in the 30-35 forward P/E ratio range, yet is now in the 46 forward PE ratio range.

It’s likely we trim here a bit here and re-allocate (perhaps to Snowflake tomorrow). The position is large so the trim is not for lack of conviction, we can promise you that. We also won’t hesitate to buy back again.

Chart: Nvidia Forward PS and PE Ratio

 

Semiconductor Q3 2022 Overview

Posted on by io-fund

This article was originally published on Forbes on Aug 12, 2022,01:21 pm EDTForbes on Aug 12, 2022,01:21 pm EDT

Semiconductor stocks have gained prominence due to growth drivers such as artificial intelligence, high-performance computing, 5G, robotics, machine learning, and electric vehicles. Despite semiconductor companies underperforming YTD, there is evidence that more supply will come online by the end of the year that will be met with equal or greater demand. Here is what AMD stated in their most recent earnings call:

“Certainly, on the Embedded side, we were supply constrained in the second quarter. And even on the Server side, we were tight in the second quarter. We have additional supply that’s coming online, especially as we get towards the end of the year. That will help us really meet more of the demand from customers. So, we feel pretty good about all of those puts and takes.”

Below, we review the stocks in the sector to find out which companies stand out in terms of revenue growth, profits, cash flows, and earnings surprise.

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Top Semiconductor stocks with the highest revenue growth rates for the current fiscal year

Chart: Revenue Growth Estimate for Current Fiscal Year

Revenue Growth Estimate for Current Fiscal Year – SOURCE: YCHARTS AND SEEKING ALPHA

Indie Semiconductor is leading with the expected year-over-year growth of 131% in the current fiscal year. The company is benefitting from the growth trend in advanced-driver assistance systems and electric vehicles. The company expects to be profitable by the end of 2023. The company has a Serviceable Addressable Market (SAM) of $40 billion by 2026. The company supplies chips and software to the automobile sector. Its chips power sensor capabilities like LiDAR and Radar, and vehicle electrification.

Monolithic Power Systems (MPWR) is expected to grow 50% in the current fiscal year. The company’s recent Q2 2022 results were strong. Revenue grew by 57% YoY to $461 million, beat the analysts' estimates by $30.41 million. The adjusted EPS came at $3.25 and beat estimates by $0.31. The Storage & Computing revenue grew by 112% YoY to $122 million; enterprise data revenue grew by 118% YoY to $65 million, and automotive grew by 25% YoY to $61 million. The management expects Q3 revenue of $490 million, representing a 51% YoY growth at the mid-point of the guidance. It was also significantly higher than the analysts' initial estimate of $400 million.

Top Semiconductor stocks with the highest revenue growth rates for the next fiscal year

Chart: Revenue growth estimate for the next fiscal year

Revenue growth estimate for the next fiscal year – SOURCE: YCHARTS AND SEEKING ALPHA

Aehr Test Systems has developed a unique technology that provides tangible benefits for testing emerging semiconductor components, such as silicon carbide and silicon photonics. Silicon carbide (SiC) is increasingly being used in EVs, while silicon photonics is being integrated into edge computing data centers. Tesla was the first to start using SiC in its vehicles with its Model 3. More EV manufacturers could follow suit due to SiC’s ability to withstand hostile conditions, improve efficiencies, and lower failure rates.

The company’s recent fiscal year ending May 2022 results were strong as revenue grew by 206% YoY to $50.8 million. The adjusted net income was $11.7 million or $0.42 per share compared to an adjusted net loss of $3.2 million or $(0.13) per share in the previous year. The management has guided revenue of $65 million for the FY ending May 2023, representing a YoY growth of 28% at the mid-point. The analyst expects revenue to grow 22% in FY ending May 2023 and 60% in the next fiscal year ending May 2024.

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Semiconductor Stocks with Top Forward P/S multiples

Chart: Semiconductor Stocks with Top Forward P/S multiples

PS Ratio (Forward) – SOURCE: YCHARTS

The companies that outperform the market deserve a premium valuation. Nvidia is leading the sector. Nvidia has a solid long-term growth prospect in AI data centers and from the automotive chips. Similarly, Wolfspeed, which is a leading company in Silicon Carbide Technology, has a premium valuation.

Ambarella is another notable company trading at a fwd P/S ratio of 10. The company’s chips which were previously popular for using in drones and cameras have recently found a niche in the automobile sector. The company’s AI computer vision chips benefit from the Internet of Things, ADAS, and autonomous driving.

Quarterly Revenue Surprise

Chart: Quarterly Revenue Surprise

Quarterly Revenue Surprise – SOURCE: YCHARTS

Semiconductor Equipment Company ACM Research crushed the analyst’s consensus revenue estimates by 44%. The company’s Q2 revenue grew by 94% YoY to $104.4 million. The revenue also included $12.9 million that could not be shipped in Q1 due to the Covid-related restrictions in China. The company also maintained the revenue guidance for the year 2022 in the range of $365 million to $405 million, representing a YoY growth of 48% at the mid-point of the guidance.

Texas Instruments beat analysts' revenue estimates by 12%. The company’s Q2 revenue grew by 14% YoY to $5.2 billion. Susquehanna analyst Christopher Rolland in a note to the clients said, "[Texas Instruments] reported better results and guidance, in part as management overestimated China shutdown impacts of ~10% of [second-quarter] sales (~$500mln), and in part on the back of solid Automotive and Industrial demand,"

Top ranked semiconductor stocks based on Free Cash Flow Margin

Chart: Top ranked semiconductor stocks based on Free Cash Flow Margin

Top ranked semiconductor stocks based on Free Cash Flow Margin – SOURCE: YCHARTS

Companies with a high cash flow margin also have a premium valuation. ASML Holding is leading the sector with the highest free cash flow margin. This is an important financial metric in the current environment, and we have noticed in the last few earnings seasons that shares were sold off when companies fell short on this metric.

Top ranked semiconductor stocks based on Net Profit Margin

Chart: Top ranked semiconductor stocks based on Net Profit Margin

Top ranked semiconductor stocks based on Net Profit Margin – SOURCE: YCHARTS

Texas Instruments leads the sector in this metric with a 44% net profit margin in the company’s recent quarterly results. Leading foundry, Taiwan Semiconductor, ranks second with a 41% net profit margin. TSMC’s revenue growth was strong, with good profits and cash flows also helped by the hike in chip production prices for its clients.

Royston Roche, Equity Analyst at the I/O Fund, contributed to this article.

Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.

AMD Q2 2022 Earnings: Feeling Zen

Posted on by io-fund

AMD’s earnings report this quarter was a win for our deep dive analysis as it confirmed our ongoing thesis from 2020-to date that AMD will continually take market share from Intel.

We discussed this in our original analysis in early 2020, our follow-up analysis in mid-2020, our 1-hour AMD webinar in 2021 and our most recent Kings of Tech special report. Most of this is summarized in the last earnings coverage from Q1 here.

Following Q2 of 2022, AMD’s product dominance over Intel has never been clearer.

AMD reported an 83% YoY increase in revenue in the data center compared to Intel’s 16% decline but under the hood, there is something much bigger going on than one quarter of performance. As an analyst pointed out, AMD appears to have gained 6% market share, which is “the highest share gain in the data center business that [AMD] has reported even going back to 2005.”

We began covering AMD when it had 4% total market share versus 96% Intel and the recent gains places AMD now in the “mid 20%” total market share for the data center against Intel. When asked if this was the correct math, Lisa Su stated: “So, I think your math is in the ZIP code from our point of view. And we are pleased that we are gaining share.”

I want to make sure that this enormous win is well understood because the market certainly didn’t reward AMD following the report. The price action will take care of itself over time.

Is our data center thesis now lagging? No – because we have the 5-nanometer line up being released in Q4 which includes Zen-4 architecture plus the Zen-5 architecture in 2024. The company also stated that the Zen-3 Milan Series is still outstripping supply with visibility six quarters out, implying for full year 2023. As a reminder, Zen-2 was Lisa Su’s comeback and Zen-3 is responsible for the current move in data center market share.

There’s also a lot to look forward to. I owe you a deep dive on AMD’s next 5 year thesis, which will include Xilinx. This acquisition is more offensive for growth rather than defensive (along the lines of how YouTube impacted Google or Instagram impacted Facebook). There is already evidence of the synergies between these two with 20% sequential growth from Xilinx in the most recent quarter.

Lisa Su and her team also quelled fears of a 2023 slowdown in the data center, and similar to Microsoft, was able to provide a “light at the end of the tunnel” type macro discussion as both are bellwethers in their respective sectors.

Brief Overview of Q2 Earnings

AMD reported revenue growth of 70% at $6.55 billion which came in at analyst expectations of $6.53 billion. EPS also came in as expected at $1.05 EPS reported versus $1.04 EPS expected. The market has given a muted tone to the earnings due to guidance provided of $6.7 billion compared to $6.83 billion expected. Meanwhile, full year revenue guidance was reiterated at $26.3 billion for 60% YoY growth.

Gross margin is a bright spot for AMD right now. The company is not only expanding its GM by 640 bps this year to 54% but the company is guiding for an additional 300 bps to 57% for next year. The company’s gross profits grew by 65% YoY to $3.03 billion.

The GAAP gross margin is at 46% and 47% last quarter. It was lower because of the amortization of intangible assets associated with the Xilinx Acquisition. Management also guided for adjusted operating margin of 24.5% for FY2022.

Certainly, the bottom line continues to be exceptional – although this may moderate in 2023 as more supply comes back online. Adjusted operating income of $1.9 billion was up 115% with an adjusted operating margin of 30% compared to 24% in Q2 2021. Adjusted net income of $1.7 billion was up 119% with an adjusted net profit margin of 26% compared to 20% in Q2 2021.

Adjusted EPS came at $1.05 (beat estimates by $0.01) compared to $0.63 for the same period last year. The company reported a total of $1.02 billion in expenses related to the amortization of acquired intangible assets in the recent quarter. The GAAP EPS was a miss due to Xilinx acquisition at $0.22 EPS compared to $0.58 EPS in the year ago quarter.

The company is reporting record operating cash flow of $1.04 billion and free cash flow of $906 million. The company has $6 billion in cash with $2.8 billion in debt. Buybacks are another bright spot for AMD with $920 million in the most recent quarter and $7.4 billion in buybacks remaining.

Overview of Segments:

Data Center:

Data Center revenue of $1.5 billion up 83% YoY was driven by EPYC processors for both cloud and enterprise customers. The company reported operating income of $472 million with AMD’s margin expansion driven primarily by this segment. The impressive growth was driven by 60 new instances across all major cloud providers.

At the time that Intel delayed its Sapphire Rapids release (againagain!), AMD stated they are “on track to launch Genoa and ramp production of Genoa” which will “position our data center business for continued growth and share gains.” The company confirmed that customer pull is very strong for their 5-nanometer CPUs for Q4 and into 2023:

“The visibility with our customers, especially our large cloud customers’ second half of this year into next year is very good. And we’re planning really for the next four to six quarters, and that gives us good visibility.”

Certainly, AMD is not getting off that easy on such a strong statement as most analysts were modeling and (loudly) predicting a slowdown in 2023 off the incredible growth both AMD, Nvidia and a few others have seen in the data center.

One analyst asked the following: “But we see all these media reports about the cloud players wanting to control their spending levels, etcetera. When do you think that shows up in their spending outlook? Or do you think you have enough of a share gain story with Genoa coming out later this year to offset any slowdown from just a broader spending environment perspective?”

Lisa Su’s answer was quite simple – to paraphrase, it’s product:

“But from our current view, I think we have a strong opportunity to continue to grow the Data Center business into 2023. And our view is we have an expanding portfolio as well. In addition to Genoa, we have our Bergamo, which is a cloud optimized capability as well that’s coming online early next year. So there is a lot of new products that are supporting sort of our growth ambitions.”

And this was followed up with a question on how much of AMD’s growth projections for 2023 are in contrast to Intel (if Intel continues to delay releases and/or Sapphire Rapids has perceived issues such as bugs, then this is a natural tailwind for AMD).

“Relative to your overall question, I think we do feel like we’re in a share gain position. I think the product positioning is such that Milan is very, very strong right now. And we think that Genoa as well is very well positioned into next year.I think we do feel like we’re in a share gain position. I think the product positioning is such that Milan is very, very strong right now. And we think that Genoa as well is very well positioned into next year.

So we’ll always spend time with the customer set and see what they’re seeing. But from our current view, I think we have a strong opportunity to continue to grow the Data Center business into 2023. And our view is we have an expanding portfolio as well. In addition to Genoa, we have our Bergamo, which is a cloud optimized capability as well that’s coming online early next year. So there is a lot of new products that are supporting sort of our growth ambitions.”In addition to Genoa, we have our Bergamo, which is a cloud optimized capability as well that’s coming online early next year. So there is a lot of new products that are supporting sort of our growth ambitions.”

Translation: Not only does AMD offer the highest performing general purpose CPUs for servers, which is primarily what is being discussed above, but the company’s lead will be further cemented when the 5nm is released in Q4. In addition, AMD’s strategy to diversify to workload specific CPUs, and also DPUs with the Pensando acquisition, and GPUs, will support continued growth in 2023.

Client Segment:

The Client Segment was up 25% YoY to $2.2 billion with operating income up 32% to $676 million. This is up 31% from $538 million. This was primarily driven by Ryzen mobile processors.

There were so many headlines over past three months about the impending “PC slowdown.” Here is what the boogeyman was:

“We have taken a more conservative outlook on the PC business. So a quarter ago, we would have thought that the PC business would be down, let’s call it, high single digits. And our current view of the PC business is that it will be down, let’s call it, mid-teens. And that’s contemplated into our third quarter guidance. And then as we go into the fourth quarter, what we see is, again, the sequential growth there will be led by the Data Center, as well as our Embedded business, with the same view of the PC business.”So a quarter ago, we would have thought that the PC business would be down, let’s call it, high single digits. And our current view of the PC business is that it will be down, let’s call it, mid-teens. And that’s contemplated into our third quarter guidance. And then as we go into the fourth quarter, what we see is, again, the sequential growth there will be led by the Data Center, as well as our Embedded business, with the same view of the PC business.”

There was additional breakdown regarding the guidance and how it takes into account PCs:

“And we are being more conservative in our PC outlook. Our PC outlook now at mid-teens would kind of put the market at somewhere around, let’s call it, 290 million to 300 million units. So I do think we’ve appropriately de-risked the PC business.”

AMD stated again the company is forecasting the PC supply (for their company) will be balanced by the second half of the year:

“I think there was a bit of buildup in PC inventory, and we’ve taken that into account in the second half. We think the AMD portion of that is modest. And as a result, it will rebalance itself in the second half of the year.”

Gaming Segment:

AMD’s gaming segment was up 32% YoY for $1.7 billion in revenue with operating income of $187 million, or 11% of revenue, compared to $175 million, or 14% a year ago. The lower operating margin was due to lower graphics revenue. The company stated that gaming graphics declined in Q2 and the gaming graphics market is expected to be down in Q3. However, management also stated they are expecting sequential increases in gaming at/around Q4.

“We do expect, as we go into the fourth quarter, though, that we’ll see some sequential increase in that business [gaming] because we’ll have new products that are launching in that timeframe.”

Embedded Segment:

This is the “Xilinx segment” and certainly this earnings report made that evident as Embedded grew 2,228% to $1.3 billion as a result of combining the two companies.

AMD did state that on a pro-forma basis, the Xilinx portfolio grew 20% sequentially. This was accelerated by AMD’s manufacturing scale and other large-scale resources for the supply chain. The company stated that both Data Center and Embedded are expected to grow fast enough to make up for the softer PC market. Embedded also helps strengthen the gross margins and Xilinx was accretive to AMD in that regard.

Although no other specifics were provided, the company pointed out there was “record core market revenue” for Xilinx, including aerospace and defense, industrial vision and health and test and measurement. Xilinx is also strong in 5G and automotive. In automotive alone, AMD believes there is a $10 billion opportunity by combining the two companies.

Macro Outlook:

AMD mentioned many times on the call that they believe supply will more evenly match demand by Q4 and into the early part of next year. This could be AMD-specific due to a strong management team along with Taiwan Semiconductors output resulting in outlier levels of supply, but generally speaking, more supply should result in deflationary pressure. AMD does not see more supply as a headwind to growth, rather the company believes it will result in more sales as demand is better matched with supply.

“And to your question about supply, we have spent basically the last 12 months building our capacity across the world to support the type of growth that we think the product can handle. So there is a large step-up in supply that we expect to see over the next four, five quarters.”there is a large step-up in supply that we expect to see over the next four, five quarters.”

“Certainly, on the Embedded side, we were supply constrained in the second quarter. And even on the Server side, we were tight in the second quarter. We have additional supply that’s coming online, especially as we get towards the end of the year. That will help us really meet more of the demand from customers. So we feel pretty good about all of those puts and takes.”, especially as we get towards the end of the year. That will help us really meet more of the demand from customers. So we feel pretty good about all of those puts and takes.”

“But overall, the 7% increase [in gross margin], I think, is very well supported given all of the new product ramps that we have going on in addition to some additional supply that’s coming in as we get into the fourth quarter.”in addition to some additional supply that’s coming in as we get into the fourth quarter.” 

“As we look into the second half of the year, we are still a bit constrained in certain areas, certain parts of the Xilinx portfolio, although we continue to make good progress. And I expect additional supply to come on, especially towards the latter part of the year, into 2023. Our view of the business, again, I think the quality of the design wins, the quality of the overall – when you look – the diverse market is very strong. And so I think as we are able to continue to relieve some of those supply constraints into the second half of the year, I think see a good growth trajectory for the business.”And I expect additional supply to come on, especially towards the latter part of the year, into 2023. Our view of the business, again, I think the quality of the design wins, the quality of the overall – when you look – the diverse market is very strong. And so I think as we are able to continue to relieve some of those supply constraints into the second half of the year, I think see a good growth trajectory for the business.”

Conclusion:

AMD’s comeback is truly historic and this quarter did not disappoint. Not only did AMD report the highest share gain in the data center business going back to 2005 but Intel’s revenue declined 22% year over year and missed consensus by 14%, which was Intel’s largest top-line disappointment since 1999, according to Refinitiv data. Intel ended the quarter with a $454 million net loss, compared with net income of $5 billion in the year-ago quarter.

This is not a coincidence. It’s due to AMD’s product excellence and gravity-defying management. Maybe the stock didn’t get the attention it deserves following Tuesday’s report, but from my estimation, it’s only a matter of time until price catches up to the market leader that is executing at scale.

I/O Fund in the Media: Semiconductor Stocks, CHIPS Act, and Why We are Bullish on Bitcoin

Posted on by io-fund

Lead Tech Analyst Beth Kindig joins Charles Payne of Fox Business news to discuss the $52B CHIPS Act, FABS Act, opportunities in tech that may be overlooked, and why I/O Fund is bullish on Bitcoin right now. 

CHIPS Act – What Semiconductor Stocks Will Benefit The Most?

While it’s a big week for tech earnings, we’re also keeping an eye on activity on Capitol Hill as we wait for a decision on the CHIPS Act which could bring $52B in subsidies and investment tax credits to boost US manufacturing. Beth and Charles discuss the question on every investor's mind: “Who can benefit from this?” 

“The CHIPS Act as it’s written will only benefit manufacturers,” Beth says. “Those manufacturers that only focus on design aren’t happy about this – rightfully so – because, again, it’s slanted in favor of manufacturers.” Beth goes on to explain the FABS Act which offers a manufacturing credit and a credit for chip design activities and that, according to the bigger chip companies, is more fair. 

Ultimately this is a positive thing. We could potentially bring chip manufacturing over to American soil. Chips are becoming the way forward in tech, so outsourcing the manufacturing where another country controls has become a source of tension. With that said, ideally it would be more evenly split between manufacturers and design activities, as the goal is to make sure the government doesn’t get in the way of innovation by weakening our strongest design companies. 

As the Acts are written and if they’re passed, they stand to benefit Intel, Micron, Texas Instruments and Lam Research – which are all FABS on American soil. As Charles points out, it also will help Applied Materials in the long run.

Opportunities in Tech that may be Overlooked

Every time there’s a bump in the market we see the mega-cap names that do pretty well. Charles asks Beth about the second-tier, non-profitable tech names that seem to be doing well, but are potentially being overlooked. 

“What we saw is there were a couple of cloud stocks and cybersecurity stocks that bottomed in May,” Beth explains. “That means as the broader market made a new low, these companies did not make a new low. From the FAANGs – Google was the one that didn’t make a new low, that’s always very encouraging to see.”

Beth’s Favorite Name in Tech Right Now 

Bitcoin – Despite crypto being out of favor, we’ve been buying in the crypto space lately. The way we will know if Bitcoin is in a larger uptrend (bulls in control) is the price has to stay above $14,000 to $15,000. This is a line in the sand. Due to sentiment, we could see one more minor pullback, and if this pullback holds the $19,000 region, then that’s a strong buy signal.

“Fundamentally, Bitcoin is in a much better position than when it traded around this price previously,” Beth stated. She notes that Bitcoin wallets have gone exponentially up, and companies such as Tesla, Square and others hold it on their balance sheets – meaning Bitcoin is certainly fundamentally stronger today. 

Want to see more from Beth? Follow her on Twitter, and subscribe to her FREE newsletter where she delivers deep-dive analysis to your inbox every week. 

If you’re a serious investor looking to take the next step, learn more about our premium membership. 

Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.

Nvidia: A Leader in AI Hardware and AI Software

Posted on by io-fund

If you were to guess, when do you think we wrote the following paragraph?

“When a thesis is not reflected in the revenue segments yet, there are typically lower entry points and ongoing volatility. You’ll see in the technical analysis that although I could not be more bullish on this stock long-term, there is weakness in the semiconductor sector and we hope this translates to a lower entry point for our readers.

The market is also in a fierce debate between AMD, Intel, and Nvidia and is also distracted by other chips, such as Micron and NXP. In my analysis, I look for growth. How big is the market relative to how big the company is now?

You can ignore Nvidia’s gaming revenue and other segments for the main trajectory that we are focused on. Gaming is great for stability and earnings reports, but the growth will not be from gaming (a market where Nvidia is already a mature, market leader). I’m also not focused on PC sales or the CPU-powered cloud, as the first is not a growth market and the second is not the piece in the cloud stack that will accelerate future technologies.”

That was written in 2019 yet the far majority of those concerns could be stated verbatim right now. Do we care about PC sales or gaming consoles? No, although our stance is that we have to expect these concerns will affect our semiconductor positions at times. The good news for Nvidia and AMD investors is that as time goes on, the less consumer-related hardware will have an impact. The 2022 Nvidia Investors Presentation provided numbers which show in detail how consumer exposure will become less of a concern in the future for these AI heavyweights.

When do you think we wrote this analysis?

“Over the past few weeks, I have read many lagging explanations on the chip shortage – too many fabless semiconductor companies, too few foundries, automobile manufacturers paused ordering in March and didn’t prepare for the sharp rebound, tensions with China, and even a fire at the Asahi Kasei plant that specifically manufactures sensing devices for the automobile industry.

While all of these are true, the overarching issue is that the role of semiconductors has changed from a commodity to the primary accelerant of future technologies. This is because connectivity, automation, and ultimately AI, will disrupt every corner of every industry.

We saw this happen with data and cloud but now we must accelerate this to the next level for AI/ML and the common denominator is semiconductors. Automotive is only the beginning. We can add renewables to the list and even e-commerce as AR/VR and AI/ML attempt to prop up the leaders who are competitive enough to add these features first.

As a tech stock analyst, I don’t have the luxury of lagging analysis of any kind. My subscribers require (and deserve) forward-looking, and with my intense focus on semiconductor chips, I don’t think my readers are surprised that semis are under pressure due to an increasingly important role.

I have repeated (perhaps too many times) that there is no way forward without the semis. We are seeing this manifest in automotive right now, but as investors, we should get used to hearing about semiconductor shortages.

You and I can debate Palantir, Snowflake or C3.AI, for example, and the valuations or the right angle for AI/ML-driven software, but the common denominator to these companies is the need for semiconductors to drive forward AI and 5G.

Now, we add the enormous push for auto manufacturers to compete with Tesla, Apple, Lucid Motors and what we have is a bottle neck where the automotive industry filters into semiconductors.

My guess is the demand won’t be letting up for many years as we are no longer in the cyclical pattern that semis are notorious for. Instead, demand will outpace supply for years to come.

Is this a bad thing or a good thing for our stocks? As investors, we can either listen to the news or listen to management. In this case, they are not aligned. Machines trade off news and natural language processing (NLP) but as human investors, we have the advantage of looking deeper into the issues.

I have written volumes of analysis leading up to the triple-digit growth we are seeing now in the data center from AI accelerator chips. Most of this was written when data center growth was negative. For instance, my Nvidia thesis was set end of 2018 — and in 2019 Nvidia reported negative data center revenue year-over-year for four quarters in a row.reported negative data center revenue year-over-year for four quarters in a row.

I mention this because following a trend’s trajectory is more important than immediate gratification from the market. The trend will always win out over time.

I have maintained that chips will eventually lead the AI market and are the best angle for investing in edge computing. I have also defended our stocks against custom silicon. Now we have the first of what I predict will be many semiconductor shortages and bullish to me.

The shortage is that there are hundreds (thousands really) of companies that rely on semiconductors. This will come to a head with AI and 5G as those who go-to-market soon with these features will have an enormous competitive advantage.”

That was written at the height of the bull market in February of 2021. My goal is to illustrate there has always been headlines to worry about for the semiconductors. We’ve firmly held these stocks and bought during dips. In the past, from 2018-2019, I focused on the GPU-powered cloud and the CUDA moat here and here. Our 2020 coverage centered on the A100 GPU which we discussed at time of launch for premium here and continued coverage on the A100 about a year later on the free side.

Here is background on the A100:

“Nvidia released the Ampere architecture and A100 GPU as an upgrade from the Volta architecture. The A100 GPUs are able to unify training and inference on a single chip, whereas in the past Nvidia’s GPUs were mainly used for training. This allows Nvidia a competitive advantage by offering both training and inferencing. The result is a 20x performance boost from a multi-instance GPU that allows many GPUs to look like one GPU. The A100 offers the largest leap in performance to date over the past 8 generations.”

Nvidia's AI Dominance Will be Propelled Forward by Software:

I wanted to go back through a bit of Nvidia’s history – what was the thesis and how did the thesis evolve? – before I go into how Nvidia will continue to dominate. In my opinion, I believe this is the most important analysis I have ever written on Nvidia because the company is changing rapidly into a software company.

The shift that Nvidia is going through has gone unnoticed and that’s to our benefit. Because we have been hell bent on finding what companies will dominate AI hardware, I’ve been asked frequently who do I think will dominate AI software (Palantir? Snowflake? Google?)

I’m prepared to give you that answer today: I believe Nvidia will be one of the biggest or perhaps the biggest AI software stack company in the world.the biggest AI software stack company in the world. The analysis below kickstarts our in-depth coverage on this new thesis — and I fully believe I will be quoting this analysis in five years from now when we check back on how the AI software thesis played out.

Before I go into semiconductor jargon where I risk losing your attention, I want to make sure our Members are fully aware that the segment where Nvidia will dominate with AI software is automotive. I am not talking about a few OEMs that trickle into a little bump in revenue. I am saying that Automotive is scheduled to become Nvidia’s number one segment – even over data centers – and to the tune of it being 3X larger than its gaming segment.

Don’t take my word for it because the CFO said exactly that (more on this below) and there is ample evidence that this is happening, which I also detail for you. Wall Street won’t be giving this the credit it deserves until 2023 at the earliest but you will hear non-stop “Nvidia Automotive” coverage by 2024-2026 as this segment ramps. I go over why those are the target dates below.

But first, let’s talk about the H100 and how this new GPU architecture will also help Nvidia lead on AI software at the enterprise level. There is plenty going on outside of Automotive that we need to cover so I kept automotive for last.

GTC Highlights: The Hopper H100 GPU

In March at GTC 2022, Nvidia announced the Hopper H100 GPU with 80 billion transistors and will be released in Q3 of this year. For reference, the A100 has 54 billion transistors. This is Nvidia’s solid attempt to keep their stake in the ground in leading high-performance computing over AMD’s Instinct MI250/250X and the newly announced MI210.

It’s easy to focus on hardware with Nvidia (and AMD) yet these companies are becoming more software-driven each year. By owning the majority of the market for AI accelerators, these two companies are afforded an opportunity to also own the software layer as a means to lower the barrier to entry for training models, deploying inference across various frameworks, and other workloads related to deep learning, conversational AI, video conferencing algorithms, and more. By supplying the hardware, these companies have natural inroads to machine learning operations (MLOps).

The H100 is the New Artificial Intelligence Infrastructure

DGX, DGX Pods and DGX SuperPods:

The H100 will power all AI and high-performance computing systems including the PCI express accelerator for mainstream servers and many H100 GPUs can be combined to power advanced AI through the following systems: DGX, DGX Pod and DGX SuperPod.

The difference between the A100 and H100 is the performance will be two to three times faster. The H100 GPUs and the DGX H100 server pods and super pods offer Nvidia the next leg-up as the company has solved an important bandwidth issue.

Hopper tackles some of the bigger issues around previous generations like speeding up algorithms by offering dynamic programming on GPUs to break down problems to simpler subproblems, boosting bandwidth by 3X with SHARP in-networking computing and Infiniband Switches, and the H100 can leverage NVLink to connect eight H100s into one giant GPU for 640 billion transistors, 32 petaflops, 640GB of HBM3, and 24 terabytes per second of memory bandwidth.

The chip is custom built by Taiwan Semiconductors with a 4nm design making it the world’s fastest 4nm GPU. The H100 has about 50% more memory and interface bandwidth than the A100. That’s 1.5X more bandwidth with the NVLink connection and PCIe 5.0 doubling the bandwidth of PCIe 4.0. The H100 will ship with support for 80GB of HBM3 memory at 3 TB/s speed.

The NVLink is now able to link together server nodes to build a data center-sized GPU. NVLink was originally designed to bypass the PCIe slot and has become an important tool for chip-to-chip connectivity, especially for high-speed operations. There is a dedicated chip called the NVSwitch which has increased the NVLink’s bandwidth. The ultimate goal is to run 32 servers with their own operating systems to run a single job. NVLink will complement the InfiniBand networking for high-performance computing and NVLink will be default for all of Nvidia’s chips, including GPUs, CPUs, DPUs and SoCs.

Where the H100 really stands apart is the leap in performance with about 3X more performance than the A100 and the H100 is up to 6X faster. The leap in performance is measured by H100’s ability to deliver up to 4,000 TFLOPS of FP8 compute, 2,000 TFLOPS of FP16 compute and 1,000 TFLOPS of TF32 compute and 60 TLOPS of general purpose FP64 compute. The A100 lacked support for FP8 compute at default whereas the H100 will leverage a transformer engine to switch between FP8 and FP16, depending on the workload.

According to Nvidia, the H100 delivers 9X more throughput in AI training, and 16X to 30X more inference performance. The company also states in HPC application-specific workloads, the H100 is 7X faster. The goal of the H100 was not only to add more transistors and make the H100 faster, but to also offer function-specific optimizations. This is achieved through the transformer engine.

The architecture aims to answer one of the bigger challenges facing superfast compute, which is that moving data into traditional servers overloads the CPU and system memory and becomes bottlenecked by PCI-Express.

By improving the bandwidth issue, Nvidia’s goal is to create more demand for their DGX Pod and SuperPod Systems, which in turn, will create more demand for their software.

The H100 DGX Pod is a 32-node, 256-GPU system. The H100 DGX Pod connects 32 DGX systems using the NVLink Switch System to scale into a super-GPU capable of 768 terabytes per second. To compare, the entire internet requires 100 terabytes per second. This results in 1 exaflop of AI computing.

From there, multiple H100 DGX Pods can connect through the Infiniband Switch to scale DGX Superpods with thousands of H100 GPUs. DGX SuperPods are turnkey systems that power enterprise AI. DGX SuperPods were also available with the A100 yet the H100 will have 6X better performance with 1 exaflop of FP8 AI performance to run trillions of parameters (more on this below).

Spectrum-4 Ethernet Platform

Perhaps one of Nvidia’s most important advancements for the H100 is the ability to attach the network directly to the GPU to avoid bottlenecks at the CPU. This is accomplished by sending data with direct memory access at 50 gigabytes per second. Hopper HGX and DGX are networking and interconnects that facilitate moving data with an advanced networking processor called the CX7. The result is the H100 CNX that avoids bandwidth bottlenecks and frees the CPU and system memory to process other parts of the application.

The Spectrum Ethernet platform, which consists of a Spectrum-4 Switch, CX7 SmartNIC and Bluefield-3 DPU will be used for several of Nvidia’s AI platforms, such as Riva, Merlin and Omniverse. These workloads include natural language processing, recommenders, and digital twins and will be supported by a networking system that helps exchange large databases between nodes. Whereas traditional workloads required many connections exchanging small amounts of data, the workloads of the future will require data to be shared quickly between GPUs and storage. This is accomplished by bypassing the CPU and sending data directly to the GPU while using the network hardware to move the data.

This is ideal for enterprise use cases where people are more likely to use Ethernet while AI and HPC workloads continue to use the Quantum-2 based off Mellanox’s InfiniBand. Quantum-2 allows for in-network computing to do data reductions in the network. It’s also more likely that Ethernet is used for 5G and sensors.

Eos: The First Hopper AI Factory

Nvidia is building AI factories to compete with AI supercomputers, which are blueprints for AI infrastructure that can be adopted by cloud partners and enterprises.

Eos is built with 18 H100 SuperPods, with 576 DGX H100 systems and 360 NVLink Switches. Nvidia states EOS is 1.4X faster than the fastest supercomputer and offers 4X the AI processing of the world’s fastest supercomputer. This will deliver 18 EFLOPS of FP8 AI compute or 9 EFLOPS of FP16 compute.

Previously, FP16 was the standard for AI whereas FP8 is gaining more support to become the industry standard. Depending on what AI compute you use, benchmarks will not be apples-to-apples if FP8 is compared to FP64 performance. Right now, AMD’s Frontier supercomputer is #1 with 1.1 exaflops of FP64 performance compared to the upcoming Venado supercomputer’s 10 exaflops of FP8 performance.

The difference is that the smaller bit size allows for an economical way to achieve more speed when giving up a small amount of accuracy doesn’t make a critical difference. This also helps in the face of a slowing Moore’s Law. FP8 is most commonly used for inference yet may be used for training in the future due to boosting throughput. Following the release of the Hopper H100, Intel released Gaudi2 which supports FP8. Chip makers Graphcore, AMD and Qualcomm have recently pushed for an industry-standard for the low precision floating point format FP8 rather than integer formats.

Here is what Nvidia said in the GTC keynote:

But the trend in AI computing has been toward developing neural nets that lean on the lowest precision that will still yield an accurate result. The smaller formats compute faster and more efficiently, and they require less memory and memory bandwidth. The addition of 8-bit floating-point units in the H100 leads to a significant speedup—double the throughput compared to its 16-bit units”

DPX Instructions (ISA):

The H100 improves dynamic programming with DPX Instructions that will help specific AI Algorithms to perform up to 7X faster than previous GPUs and 40X faster than CPU-based algorithms. As algorithms require more complexity, the new set of DPX instructions will help break the complex problems down into simpler subproblems using GPUs instead of CPUs or FPGAs.

The DPX ISA are expected to be broadly available with the CUDA 12.0 release. Examples of where this will be useful include disease research and drug discovery where the process can be sped up 35X for real-time processing to match the rate of DNA sequencing. Route optimization and finding the shortest distance between destinations for use in factories and autonomous driving systems, or Floyd-Warshall acceleration, is boosted up to 40X compared to CPU-only servers. These instructions will also be used for quantum computing and SQL queries as dynamic programming can help find the optimal order for joining a set of tables.

GPU Confidential Computing:

Data is encrypted at-rest and in-transit yet is often unprotected during use. Meanwhile, the data used to train AI models is worth millions in investments and is trained from domain knowledge and company-proprietary data. The new H100 offers confidential computing whereas previously only CPUs offered the protection of both data and applications during use.

Nvidia is Becoming a Leading AI Software Company

It would be easy to read the information above and to assume Nvidia is improving its hardware. However, the company’s future resides in software which will remove some of the cyclicality of hardware revenue. I believe once Nvidia’s software revenue begins to reveal itself in earnings reports, the market will finally piece together the true potential of this AI powerhouse.

It’s both the hardware and the software stack that led me to say Nvidia will surpass Apple in 5 years. You know this story well: the relationship between a hardware company leveraging their position to capture the lion’s share of the software — because that’s exactly what Apple did.

There are four layers to Nvidia’s full-stack accelerated computing: hardware, system software, platform software and applications. Below, I discuss a few ways that Nvidia is capturing more of the software stack due to vendor lock-in effects from their dominance in hardware.

As stated, in the past, our focus was the GPU-powered data center. This was a four-year thesis from 2018 and we doubled up on the thesis in June of 2020 for the A100 release. I want to make sure and emphasize that Nvidia’s lesser-known catalyst is actually the software.

The H100 is helpful in maintaining a lead in GPUs, which is critical turf to protect with GPUs being the most popular AI accelerator, however — the AI/ML catalyst will be further fueled by the Nvidia’s lead in software. This is why the majority of who will remain the AI leader will be up to developers and not the C-suite partnerships on hardware that characterized Intel’s lead over the past few decades. The developers choose the frameworks, the SDKs, libraries and the other parts of the software stack, and because of this, they also choose the GPUs they build on rather than IT departments.

Transformers

The transformer engine is one of the key aspects of the H100. Transformers are becoming one of the most popular neural-network models by applying self-attention to detect how data elements in a series influence and depend on one another.

Sequential text, images and video data are used for self-supervised learning and pattern recognition, which results in more data being used to create better models. Prior to transformer models, labeled datasets had to be used to train neural networks. Transformer models eliminate this need by finding patterns between elements mathematically, which substantially opens up what datasets can be used and how quickly. Transformers are partial to the parallel processing that GPUs offer.

Google first introduced transformer models in 2017 and transformers are used in Google and Bing Search. Transformers also led to BERT models, which stands for Bidirectional Encoder Representations from Transformers, and is commonly used for text sequences. Transformers are also used in GPT-3 (it’s the T in GPT) which improved from 1.5 billion parameters to 175 billion parameters. GPT-3 has the ability to report on queries it has not been specifically trained on.

Nvidia and Microsoft recently worked on a Mega transformer model with 530 billion parameters and the future for AI engineers is trillion-parameter transformers and applications. The H100 is already prepping for this. According to Nvidia, the training needs for transformer models will increase 275-fold every two years compared to 8-fold for other models. The H100 GPU with its Transformer Engine supports the FP8 format to speed up training to support trillion-parameter models. This leads to transformer models that go from taking 5 days to train to becoming 6X faster to only taking 19 hours to train.

The transformer engine is software combined with the new hardware in the H100’s tensor cores. As discussed, the A100 was designed for floating-point numbers to 16 bits while the H100 is designed for 8 bits. This is helpful because AI models are moving toward neural nets that lean on the lowest precision and yet still yields an accurate result. In this case, 8 bits double the throughput of 16-bit units, compute faster and more efficiently, and they require less memory and memory bandwidth.

The main feature from the Transformer Engine is the ability to choose what precision is needed for each layer in the neural network at each step, transitioning between 8-bits, 16-bits, 32-bits, plus the H100 is able to do matrix math with two forms of 8-bit numbers with either 5-bits as the exponent or 4-bits as the exponent: E5M2 and E4M3. This is important because the E4M3 may be favored for back propagation while E5M2 may be favored for inferencing.

Pictured above: Nvidia is prepped to support model sizes growing up to 275X every two years

Triton Inference Server:

Nvidia offers AI frameworks to reduce time for developers throughout the AI workflow from data processing and ETL to deep learning model training and large-scale inferencing. These libraries include Dali, Rapids, Triton and Magnum I/O. The library supports all popular frameworks and offers pre-trained models and data pipelines.

Triton is open-source inference software that helps developers deploy models across GPUs and CPUs, it supports Tensor Flow and PyTorch, any query type and any model – such as Transformers or CNNs (used for image recognition) and RNNs (used in speech recognition). The inference engine helps developers take AI development from experimentation to production by removing the need for multiple inference servers and simplifying machine learning infrastructure on the backend.

MLOps (machine learning operations) helps developers with less ML expertise to train and deploy models yet there were limited use cases with little help in deploying custom models. Triton offers high performance inference and scalability with Dockers and Kubernetes while serving up to hundreds of models with the model control API. By supporting all popular frameworks, Triton helps developers avoid framework lock-in due to the consistent interface regardless of training framework or hardware.

Nvidia will Power the Lion’s Share of Automotive – and that means software licensing

Nvidia’s lead in automotive across dozens of OEMs requires its own deep dive. The reason I haven’t prioritized this is because Hyperion 8 is shipping in 2024 and Hyperion 9 will ship in 2026. However, as long-term investors, we should touch base now on the long-term vision for yet another large and sweeping revenue segment. In fact, automotive promises to be Nvidia’s largest segment by 2030 – so on that alone, imagine what Nvidia investors have in front of us.

Nvidia’s Orin SoC (system-on-a-chip) is designed for the neural networks that run robots and AVs at the edge. This is the central computer for the car. The Orin SoC is capable of 254 trillion operations per second by combining Nvidia GPUs with Arm CPU cores and TensorRT APIs. The goal is to help OEMs move from Level 2 autonomous systems to the elusive Level 5 and it stiffens the competition with Tesla’s FSD. Notably, at the release two years ago, Tesla pointed towards Orin’s power consumption as a potential issue for EV batteries but this has not stopped many competing EVs from adopting Nvidia’s in-vehicle hardware and DRIVE software stack.

The EV manufacturers that have already moved forward with Nvidia DRIVE Orin include: Nio, Xpeng, BYD, Lucid Group, Mercedes and Land Rover, GM Cruise — you name it, it’s probably in production with Nvidia at this moment. The company’s current automotive pipeline exceeds $11 billion over the next six years – expect this small blip of pipeline to grow exponentially.

Nvidia recently announced an upgrade to Orin called Atlan with 1,000 TOPS on one chip, or more than then Level 5 compute in AVs today. This chip will catapult forward the computing performance of AVs and is expected to be released in 2023.

Nvidia DRIVE is the operating system and software stack for vehicles that offers an execution environment and includes both security and over-the-air updates. DriveWorks is an SDK that enables self-driving applications. Drive AV offers key ingredients to an autonomous system, such as perception, mapping and planning modules. Regarding mapping, Nvidia DRIVE Map is a multi-modal drive engine that can map independently and has two map engines. Drive IX is open-source software that offers vision, voice and graphics for the user experience. (I will do a separate deep dive on Nvidia Automotive in 2023).

The entire autonomous platform is called Hyperion, which includes the compute and sensor toolkit. This includes the hardware plus a 360-degree camera, radar, lidar and ultrasonic sensor suite. As stated, Hyperion 8 ships in 2024 with Hyperion 9 shipping in 2026, which will double the processing speed and offer an increase in sensors. Nvidia offers open-source developer kits to help increase its compatibility across various projects.

Rather than train the vehicles on the road, Nvidia trains in simulation and can create virtual world obstacles for the vehicles to learn from. This is a different approach from companies like Tesla who have millions of cars on the road collecting data which they then augment for unusual events with a photorealistic simulator.

Tesla has the most data of any car manufacturer which helps the company competitively as more data equals better performing models especially in terms of object detection. More data from millions of cars on the roads also helps with prediction as Tesla collects data from incorrect predictions that can be added to the training set. By leveraging a prediction neural network, Tesla does not need to use human labeling or annotation and can instead use what’s called a temporal sequence of events — in other words, Tesla rewinds events and labels objects automatically with the use of a supercomputer.

The advantage here is that training neural networks correlates with the miles (which again, are substantial due to size of fleet on the road compared to competitors) rather than correlating with the need for human labeling. The result of automatic labeling is that Tesla is able to predict rare situations with more accuracy.

Where Nvidia delivers a strong advantage is the company has decades of history with graphics and simulation due to its gaming roots. As stated, Tesla also uses imitation learning and has a photorealistic simulator which uses vector space for labels and functions like a game engine. However, Nvidia has been quietly working on their simulation platform for many years internally despite only recently marketing Omniverse to the public. In this case, Nvidia has such a high-level of confidence in their simulation skills that they forego the real-life fleet to primarily train virtual 3D models. The company is also packaging the simulation platform for many other uses cases, such as AI factories, 5G networks, power plants and climate research. Developers can work with 3D tools through Python-based development.

Here’s a 10-minute demonstration with the simulation platform here around minute 7:00.

To keep it simple, Tesla’s primary advantage is the data they have collected as no other EV/AV has collected this level of data from real drivers. To contrast, Nvidia has arguably the best simulation platform due to decades of graphics work. These digital twins are only now being widely marketed despite being in development for over 5 years. The license costs $9,000 and Nvidia has estimated its current addressable market is 20 million engineers. Notably, Nvidia’s Hyperion will also be deployed in millions of vehicles over time, offering similar levels of data as Tesla’s fleet.

The Tesla VS Nvidia debates have not formally begun but they are certainly in our future … so brace yourself. Ultimately, the way Nvidia stands apart is the company does not directly compete on manufacturing vehicles. This is something anyone can agree on. That means many OEMs will use Nvidia’s DRIVE system whereas Tesla is less likely to commercialize their software as they’re viewed as a main competitor.

As long as Nvidia continues to innovate and maintain a lead, the popularity of its DRIVE system is likely to remain due to the company’s strategic advantages in AI and supercomputing. The company did an excellent job of tackling the edge computing use case of autonomous vehicles first.

Hardware is only part of the equation. The long-term plan is for Nvidia to license software for autonomous vehicles, which will create a recurring revenue stream. The licensing fees will go well beyond Omniverse to include the actual owner of the vehicle paying a subscription fee to Nvidia for its software. Tesla does this with their AutoPilot software which has grown from $5,000 to $12,000 per vehicle.

The breakdown according to the 2022 Investor Presentation looks like this:

  • $100 billion from gaming
  • $300 billion from chips and systems
  • $150 billion from AI Enterprise software
  • $150 billion from Omniverse software – fees are charged to both users and robots/digital twins
  • $300 billion from Automotive – primarily software

What Nvidia is communicating is that software revenue will surpass hardware revenue long-term.

Here is what Kress stated: "Our software content per vehicle can be in the thousands of dollars over the lifetime of the vehicle compared to the hundreds of dollars for the hardware. And second, software scales with the installed base of vehicles, not annual production.”

Note on CUDA:

The software discussion on Nvidia is not complete without a mention of CUDA. We called this Nvidia’s moat back in 2018 and we continue to believe it provides an important moat. The CUDA-related libraries include frameworks that span quantum computing, robotics, 5G networks, cybersecurity and drug discovery. The universal skills taught around CUDA and Nvidia’s SDKs help to drive more business for Nvidia’s GPUs.

Note: I’ve covered Omniverse in-depth here.

Risk: Valuation

The primary risk right now is valuation as Nvidia trades 2X higher than its peers on both the top line sales valuations and on the bottom line with earnings and cash-based valuations. There’s probably equal risk in waiting for Nvidia to drop another 50% as there is in buying Nvidia at the 2X valuation. One reason Nvidia may be valued here is because it’s slowly becoming a software company. Regardless, Knox’s technicals help immensely in determining if the market will continue to award Nvidia it’s gold medal valuation or if the market will discount Nvidia based on sentiment-driven headlines. This is a position we plan to keep on building so you can keep an eye out for those trade alerts over the next few years.

Conclusion:

Finding great companies is only half the battle, fighting negative sentiment is the other half – and semis have no shortage of this in any market – hence our beginning quotes from 2019 and also 2021.

Nvidia is the strongest company in terms of product on the market today. That doesn’t mean semis won’t be a roller coaster – we should fully expect that semis will undulate in sentiment and price while we hold our stocks over many years. We can’t change the way Wall Street works — which is a pendulum that swings between value stocks and growth stocks — but we can describe in great detail why concerns around gaming and consumer electronics slowing down is not going to change our position. We do not care to perfectly time entries or to find a perfect bottom – you’ll be hard pressed to find any legendary investor recommend that this be an investor’s goal. What we care about is finding quality companies and building those positions over time. Nvidia fits this description.

Nvidia: A Leader in AI Hardware and AI Software

Posted on by io-fund

If you were to guess, when do you think we wrote the following paragraph?

“When a thesis is not reflected in the revenue segments yet, there are typically lower entry points and ongoing volatility. You’ll see in the technical analysis that although I could not be more bullish on this stock long-term, there is weakness in the semiconductor sector and we hope this translates to a lower entry point for our readers.

The market is also in a fierce debate between AMD, Intel, and Nvidia and is also distracted by other chips, such as Micron and NXP. In my analysis, I look for growth. How big is the market relative to how big the company is now?

You can ignore Nvidia’s gaming revenue and other segments for the main trajectory that we are focused on. Gaming is great for stability and earnings reports, but the growth will not be from gaming (a market where Nvidia is already a mature, market leader). I’m also not focused on PC sales or the CPU-powered cloud, as the first is not a growth market and the second is not the piece in the cloud stack that will accelerate future technologies.”

That was written in 2019 yet the far majority of those concerns could be stated verbatim right now. Do we care about PC sales or gaming consoles? No, although our stance is that we have to expect these concerns will affect our semiconductor positions at times. The good news for Nvidia and AMD investors is that as time goes on, the less consumer-related hardware will have an impact. The 2022 Nvidia Investors Presentation provided numbers which show in detail how consumer exposure will become less of a concern in the future for these AI heavyweights.

When do you think we wrote this analysis?

“Over the past few weeks, I have read many lagging explanations on the chip shortage – too many fabless semiconductor companies, too few foundries, automobile manufacturers paused ordering in March and didn’t prepare for the sharp rebound, tensions with China, and even a fire at the Asahi Kasei plant that specifically manufactures sensing devices for the automobile industry.

While all of these are true, the overarching issue is that the role of semiconductors has changed from a commodity to the primary accelerant of future technologies. This is because connectivity, automation, and ultimately AI, will disrupt every corner of every industry.

We saw this happen with data and cloud but now we must accelerate this to the next level for AI/ML and the common denominator is semiconductors. Automotive is only the beginning. We can add renewables to the list and even e-commerce as AR/VR and AI/ML attempt to prop up the leaders who are competitive enough to add these features first.

As a tech stock analyst, I don’t have the luxury of lagging analysis of any kind. My subscribers require (and deserve) forward-looking, and with my intense focus on semiconductor chips, I don’t think my readers are surprised that semis are under pressure due to an increasingly important role.

I have repeated (perhaps too many times) that there is no way forward without the semis. We are seeing this manifest in automotive right now, but as investors, we should get used to hearing about semiconductor shortages.

You and I can debate Palantir, Snowflake or C3.AI, for example, and the valuations or the right angle for AI/ML-driven software, but the common denominator to these companies is the need for semiconductors to drive forward AI and 5G.

Now, we add the enormous push for auto manufacturers to compete with Tesla, Apple, Lucid Motors and what we have is a bottle neck where the automotive industry filters into semiconductors.

My guess is the demand won’t be letting up for many years as we are no longer in the cyclical pattern that semis are notorious for. Instead, demand will outpace supply for years to come.

Is this a bad thing or a good thing for our stocks? As investors, we can either listen to the news or listen to management. In this case, they are not aligned. Machines trade off news and natural language processing (NLP) but as human investors, we have the advantage of looking deeper into the issues.

I have written volumes of analysis leading up to the triple-digit growth we are seeing now in the data center from AI accelerator chips. Most of this was written when data center growth was negative. For instance, my Nvidia thesis was set end of 2018 — and in 2019 Nvidia reported negative data center revenue year-over-year for four quarters in a row.reported negative data center revenue year-over-year for four quarters in a row.

I mention this because following a trend’s trajectory is more important than immediate gratification from the market. The trend will always win out over time.

I have maintained that chips will eventually lead the AI market and are the best angle for investing in edge computing. I have also defended our stocks against custom silicon. Now we have the first of what I predict will be many semiconductor shortages and bullish to me.

The shortage is that there are hundreds (thousands really) of companies that rely on semiconductors. This will come to a head with AI and 5G as those who go-to-market soon with these features will have an enormous competitive advantage.”

That was written at the height of the bull market in February of 2021. My goal is to illustrate there has always been headlines to worry about for the semiconductors. We’ve firmly held these stocks and bought during dips. In the past, from 2018-2019, I focused on the GPU-powered cloud and the CUDA moat here and here. Our 2020 coverage centered on the A100 GPU which we discussed at time of launch for premium here and continued coverage on the A100 about a year later on the free side.

Here is background on the A100:

“Nvidia released the Ampere architecture and A100 GPU as an upgrade from the Volta architecture. The A100 GPUs are able to unify training and inference on a single chip, whereas in the past Nvidia’s GPUs were mainly used for training. This allows Nvidia a competitive advantage by offering both training and inferencing. The result is a 20x performance boost from a multi-instance GPU that allows many GPUs to look like one GPU. The A100 offers the largest leap in performance to date over the past 8 generations.”

Nvidia's AI Dominance Will be Propelled Forward by Software:

I wanted to go back through a bit of Nvidia’s history – what was the thesis and how did the thesis evolve? – before I go into how Nvidia will continue to dominate. In my opinion, I believe this is the most important analysis I have ever written on Nvidia because the company is changing rapidly into a software company.

The shift that Nvidia is going through has gone unnoticed and that’s to our benefit. Because we have been hell bent on finding what companies will dominate AI hardware, I’ve been asked frequently who do I think will dominate AI software (Palantir? Snowflake? Google?)

I’m prepared to give you that answer today: I believe Nvidia will be one of the biggest or perhaps the biggest AI software stack company in the world.the biggest AI software stack company in the world. The analysis below kickstarts our in-depth coverage on this new thesis — and I fully believe I will be quoting this analysis in five years from now when we check back on how the AI software thesis played out.

Before I go into semiconductor jargon where I risk losing your attention, I want to make sure our Members are fully aware that the segment where Nvidia will dominate with AI software is automotive. I am not talking about a few OEMs that trickle into a little bump in revenue. I am saying that Automotive is scheduled to become Nvidia’s number one segment – even over data centers – and to the tune of it being 3X larger than its gaming segment.

Don’t take my word for it because the CFO said exactly that (more on this below) and there is ample evidence that this is happening, which I also detail for you. Wall Street won’t be giving this the credit it deserves until 2023 at the earliest but you will hear non-stop “Nvidia Automotive” coverage by 2024-2026 as this segment ramps. I go over why those are the target dates below.

But first, let’s talk about the H100 and how this new GPU architecture will also help Nvidia lead on AI software at the enterprise level. There is plenty going on outside of Automotive that we need to cover so I kept automotive for last.

GTC Highlights: The Hopper H100 GPU

In March at GTC 2022, Nvidia announced the Hopper H100 GPU with 80 billion transistors and will be released in Q3 of this year. For reference, the A100 has 54 billion transistors. This is Nvidia’s solid attempt to keep their stake in the ground in leading high-performance computing over AMD’s Instinct MI250/250X and the newly announced MI210.

It’s easy to focus on hardware with Nvidia (and AMD) yet these companies are becoming more software-driven each year. By owning the majority of the market for AI accelerators, these two companies are afforded an opportunity to also own the software layer as a means to lower the barrier to entry for training models, deploying inference across various frameworks, and other workloads related to deep learning, conversational AI, video conferencing algorithms, and more. By supplying the hardware, these companies have natural inroads to machine learning operations (MLOps).

The H100 is the New Artificial Intelligence Infrastructure

DGX, DGX Pods and DGX SuperPods:

The H100 will power all AI and high-performance computing systems including the PCI express accelerator for mainstream servers and many H100 GPUs can be combined to power advanced AI through the following systems: DGX, DGX Pod and DGX SuperPod.

The difference between the A100 and H100 is the performance will be two to three times faster. The H100 GPUs and the DGX H100 server pods and super pods offer Nvidia the next leg-up as the company has solved an important bandwidth issue.

Hopper tackles some of the bigger issues around previous generations like speeding up algorithms by offering dynamic programming on GPUs to break down problems to simpler subproblems, boosting bandwidth by 3X with SHARP in-networking computing and Infiniband Switches, and the H100 can leverage NVLink to connect eight H100s into one giant GPU for 640 billion transistors, 32 petaflops, 640GB of HBM3, and 24 terabytes per second of memory bandwidth.

The chip is custom built by Taiwan Semiconductors with a 4nm design making it the world’s fastest 4nm GPU. The H100 has about 50% more memory and interface bandwidth than the A100. That’s 1.5X more bandwidth with the NVLink connection and PCIe 5.0 doubling the bandwidth of PCIe 4.0. The H100 will ship with support for 80GB of HBM3 memory at 3 TB/s speed.

The NVLink is now able to link together server nodes to build a data center-sized GPU. NVLink was originally designed to bypass the PCIe slot and has become an important tool for chip-to-chip connectivity, especially for high-speed operations. There is a dedicated chip called the NVSwitch which has increased the NVLink’s bandwidth. The ultimate goal is to run 32 servers with their own operating systems to run a single job. NVLink will complement the InfiniBand networking for high-performance computing and NVLink will be default for all of Nvidia’s chips, including GPUs, CPUs, DPUs and SoCs.

Where the H100 really stands apart is the leap in performance with about 3X more performance than the A100 and the H100 is up to 6X faster. The leap in performance is measured by H100’s ability to deliver up to 4,000 TFLOPS of FP8 compute, 2,000 TFLOPS of FP16 compute and 1,000 TFLOPS of TF32 compute and 60 TLOPS of general purpose FP64 compute. The A100 lacked support for FP8 compute at default whereas the H100 will leverage a transformer engine to switch between FP8 and FP16, depending on the workload.

According to Nvidia, the H100 delivers 9X more throughput in AI training, and 16X to 30X more inference performance. The company also states in HPC application-specific workloads, the H100 is 7X faster. The goal of the H100 was not only to add more transistors and make the H100 faster, but to also offer function-specific optimizations. This is achieved through the transformer engine.

The architecture aims to answer one of the bigger challenges facing superfast compute, which is that moving data into traditional servers overloads the CPU and system memory and becomes bottlenecked by PCI-Express.

By improving the bandwidth issue, Nvidia’s goal is to create more demand for their DGX Pod and SuperPod Systems, which in turn, will create more demand for their software.

The H100 DGX Pod is a 32-node, 256-GPU system. The H100 DGX Pod connects 32 DGX systems using the NVLink Switch System to scale into a super-GPU capable of 768 terabytes per second. To compare, the entire internet requires 100 terabytes per second. This results in 1 exaflop of AI computing.

From there, multiple H100 DGX Pods can connect through the Infiniband Switch to scale DGX Superpods with thousands of H100 GPUs. DGX SuperPods are turnkey systems that power enterprise AI. DGX SuperPods were also available with the A100 yet the H100 will have 6X better performance with 1 exaflop of FP8 AI performance to run trillions of parameters (more on this below).

Spectrum-4 Ethernet Platform

Perhaps one of Nvidia’s most important advancements for the H100 is the ability to attach the network directly to the GPU to avoid bottlenecks at the CPU. This is accomplished by sending data with direct memory access at 50 gigabytes per second. Hopper HGX and DGX are networking and interconnects that facilitate moving data with an advanced networking processor called the CX7. The result is the H100 CNX that avoids bandwidth bottlenecks and frees the CPU and system memory to process other parts of the application.

The Spectrum Ethernet platform, which consists of a Spectrum-4 Switch, CX7 SmartNIC and Bluefield-3 DPU will be used for several of Nvidia’s AI platforms, such as Riva, Merlin and Omniverse. These workloads include natural language processing, recommenders, and digital twins and will be supported by a networking system that helps exchange large databases between nodes. Whereas traditional workloads required many connections exchanging small amounts of data, the workloads of the future will require data to be shared quickly between GPUs and storage. This is accomplished by bypassing the CPU and sending data directly to the GPU while using the network hardware to move the data.

This is ideal for enterprise use cases where people are more likely to use Ethernet while AI and HPC workloads continue to use the Quantum-2 based off Mellanox’s InfiniBand. Quantum-2 allows for in-network computing to do data reductions in the network. It’s also more likely that Ethernet is used for 5G and sensors.

Eos: The First Hopper AI Factory

Nvidia is building AI factories to compete with AI supercomputers, which are blueprints for AI infrastructure that can be adopted by cloud partners and enterprises.

Eos is built with 18 H100 SuperPods, with 576 DGX H100 systems and 360 NVLink Switches. Nvidia states EOS is 1.4X faster than the fastest supercomputer and offers 4X the AI processing of the world’s fastest supercomputer. This will deliver 18 EFLOPS of FP8 AI compute or 9 EFLOPS of FP16 compute.

Previously, FP16 was the standard for AI whereas FP8 is gaining more support to become the industry standard. Depending on what AI compute you use, benchmarks will not be apples-to-apples if FP8 is compared to FP64 performance. Right now, AMD’s Frontier supercomputer is #1 with 1.1 exaflops of FP64 performance compared to the upcoming Venado supercomputer’s 10 exaflops of FP8 performance.

The difference is that the smaller bit size allows for an economical way to achieve more speed when giving up a small amount of accuracy doesn’t make a critical difference. This also helps in the face of a slowing Moore’s Law. FP8 is most commonly used for inference yet may be used for training in the future due to boosting throughput. Following the release of the Hopper H100, Intel released Gaudi2 which supports FP8. Chip makers Graphcore, AMD and Qualcomm have recently pushed for an industry-standard for the low precision floating point format FP8 rather than integer formats.

Here is what Nvidia said in the GTC keynote:

But the trend in AI computing has been toward developing neural nets that lean on the lowest precision that will still yield an accurate result. The smaller formats compute faster and more efficiently, and they require less memory and memory bandwidth. The addition of 8-bit floating-point units in the H100 leads to a significant speedup—double the throughput compared to its 16-bit units”

DPX Instructions (ISA):

The H100 improves dynamic programming with DPX Instructions that will help specific AI Algorithms to perform up to 7X faster than previous GPUs and 40X faster than CPU-based algorithms. As algorithms require more complexity, the new set of DPX instructions will help break the complex problems down into simpler subproblems using GPUs instead of CPUs or FPGAs.

The DPX ISA are expected to be broadly available with the CUDA 12.0 release. Examples of where this will be useful include disease research and drug discovery where the process can be sped up 35X for real-time processing to match the rate of DNA sequencing. Route optimization and finding the shortest distance between destinations for use in factories and autonomous driving systems, or Floyd-Warshall acceleration, is boosted up to 40X compared to CPU-only servers. These instructions will also be used for quantum computing and SQL queries as dynamic programming can help find the optimal order for joining a set of tables.

GPU Confidential Computing:

Data is encrypted at-rest and in-transit yet is often unprotected during use. Meanwhile, the data used to train AI models is worth millions in investments and is trained from domain knowledge and company-proprietary data. The new H100 offers confidential computing whereas previously only CPUs offered the protection of both data and applications during use.

Nvidia is Becoming a Leading AI Software Company

It would be easy to read the information above and to assume Nvidia is improving its hardware. However, the company’s future resides in software which will remove some of the cyclicality of hardware revenue. I believe once Nvidia’s software revenue begins to reveal itself in earnings reports, the market will finally piece together the true potential of this AI powerhouse.

It’s both the hardware and the software stack that led me to say Nvidia will surpass Apple in 5 years. You know this story well: the relationship between a hardware company leveraging their position to capture the lion’s share of the software — because that’s exactly what Apple did.

There are four layers to Nvidia’s full-stack accelerated computing: hardware, system software, platform software and applications. Below, I discuss a few ways that Nvidia is capturing more of the software stack due to vendor lock-in effects from their dominance in hardware.

As stated, in the past, our focus was the GPU-powered data center. This was a four-year thesis from 2018 and we doubled up on the thesis in June of 2020 for the A100 release. I want to make sure and emphasize that Nvidia’s lesser-known catalyst is actually the software.

The H100 is helpful in maintaining a lead in GPUs, which is critical turf to protect with GPUs being the most popular AI accelerator, however — the AI/ML catalyst will be further fueled by the Nvidia’s lead in software. This is why the majority of who will remain the AI leader will be up to developers and not the C-suite partnerships on hardware that characterized Intel’s lead over the past few decades. The developers choose the frameworks, the SDKs, libraries and the other parts of the software stack, and because of this, they also choose the GPUs they build on rather than IT departments.

Transformers

The transformer engine is one of the key aspects of the H100. Transformers are becoming one of the most popular neural-network models by applying self-attention to detect how data elements in a series influence and depend on one another.

Sequential text, images and video data are used for self-supervised learning and pattern recognition, which results in more data being used to create better models. Prior to transformer models, labeled datasets had to be used to train neural networks. Transformer models eliminate this need by finding patterns between elements mathematically, which substantially opens up what datasets can be used and how quickly. Transformers are partial to the parallel processing that GPUs offer.

Google first introduced transformer models in 2017 and transformers are used in Google and Bing Search. Transformers also led to BERT models, which stands for Bidirectional Encoder Representations from Transformers, and is commonly used for text sequences. Transformers are also used in GPT-3 (it’s the T in GPT) which improved from 1.5 billion parameters to 175 billion parameters. GPT-3 has the ability to report on queries it has not been specifically trained on.

Nvidia and Microsoft recently worked on a Mega transformer model with 530 billion parameters and the future for AI engineers is trillion-parameter transformers and applications. The H100 is already prepping for this. According to Nvidia, the training needs for transformer models will increase 275-fold every two years compared to 8-fold for other models. The H100 GPU with its Transformer Engine supports the FP8 format to speed up training to support trillion-parameter models. This leads to transformer models that go from taking 5 days to train to becoming 6X faster to only taking 19 hours to train.

The transformer engine is software combined with the new hardware in the H100’s tensor cores. As discussed, the A100 was designed for floating-point numbers to 16 bits while the H100 is designed for 8 bits. This is helpful because AI models are moving toward neural nets that lean on the lowest precision and yet still yields an accurate result. In this case, 8 bits double the throughput of 16-bit units, compute faster and more efficiently, and they require less memory and memory bandwidth.

The main feature from the Transformer Engine is the ability to choose what precision is needed for each layer in the neural network at each step, transitioning between 8-bits, 16-bits, 32-bits, plus the H100 is able to do matrix math with two forms of 8-bit numbers with either 5-bits as the exponent or 4-bits as the exponent: E5M2 and E4M3. This is important because the E4M3 may be favored for back propagation while E5M2 may be favored for inferencing.

Pictured above: Nvidia is prepped to support model sizes growing up to 275X every two years

Triton Inference Server:

Nvidia offers AI frameworks to reduce time for developers throughout the AI workflow from data processing and ETL to deep learning model training and large-scale inferencing. These libraries include Dali, Rapids, Triton and Magnum I/O. The library supports all popular frameworks and offers pre-trained models and data pipelines.

Triton is open-source inference software that helps developers deploy models across GPUs and CPUs, it supports Tensor Flow and PyTorch, any query type and any model – such as Transformers or CNNs (used for image recognition) and RNNs (used in speech recognition). The inference engine helps developers take AI development from experimentation to production by removing the need for multiple inference servers and simplifying machine learning infrastructure on the backend.

MLOps (machine learning operations) helps developers with less ML expertise to train and deploy models yet there were limited use cases with little help in deploying custom models. Triton offers high performance inference and scalability with Dockers and Kubernetes while serving up to hundreds of models with the model control API. By supporting all popular frameworks, Triton helps developers avoid framework lock-in due to the consistent interface regardless of training framework or hardware.

Nvidia will Power the Lion’s Share of Automotive – and that means software licensing

Nvidia’s lead in automotive across dozens of OEMs requires its own deep dive. The reason I haven’t prioritized this is because Hyperion 8 is shipping in 2024 and Hyperion 9 will ship in 2026. However, as long-term investors, we should touch base now on the long-term vision for yet another large and sweeping revenue segment. In fact, automotive promises to be Nvidia’s largest segment by 2030 – so on that alone, imagine what Nvidia investors have in front of us.

Nvidia’s Orin SoC (system-on-a-chip) is designed for the neural networks that run robots and AVs at the edge. This is the central computer for the car. The Orin SoC is capable of 254 trillion operations per second by combining Nvidia GPUs with Arm CPU cores and TensorRT APIs. The goal is to help OEMs move from Level 2 autonomous systems to the elusive Level 5 and it stiffens the competition with Tesla’s FSD. Notably, at the release two years ago, Tesla pointed towards Orin’s power consumption as a potential issue for EV batteries but this has not stopped many competing EVs from adopting Nvidia’s in-vehicle hardware and DRIVE software stack.

The EV manufacturers that have already moved forward with Nvidia DRIVE Orin include: Nio, Xpeng, BYD, Lucid Group, Mercedes and Land Rover, GM Cruise — you name it, it’s probably in production with Nvidia at this moment. The company’s current automotive pipeline exceeds $11 billion over the next six years – expect this small blip of pipeline to grow exponentially.

Nvidia recently announced an upgrade to Orin called Atlan with 1,000 TOPS on one chip, or more than then Level 5 compute in AVs today. This chip will catapult forward the computing performance of AVs and is expected to be released in 2023.

Nvidia DRIVE is the operating system and software stack for vehicles that offers an execution environment and includes both security and over-the-air updates. DriveWorks is an SDK that enables self-driving applications. Drive AV offers key ingredients to an autonomous system, such as perception, mapping and planning modules. Regarding mapping, Nvidia DRIVE Map is a multi-modal drive engine that can map independently and has two map engines. Drive IX is open-source software that offers vision, voice and graphics for the user experience. (I will do a separate deep dive on Nvidia Automotive in 2023).

The entire autonomous platform is called Hyperion, which includes the compute and sensor toolkit. This includes the hardware plus a 360-degree camera, radar, lidar and ultrasonic sensor suite. As stated, Hyperion 8 ships in 2024 with Hyperion 9 shipping in 2026, which will double the processing speed and offer an increase in sensors. Nvidia offers open-source developer kits to help increase its compatibility across various projects.

Rather than train the vehicles on the road, Nvidia trains in simulation and can create virtual world obstacles for the vehicles to learn from. This is a different approach from companies like Tesla who have millions of cars on the road collecting data which they then augment for unusual events with a photorealistic simulator.

Tesla has the most data of any car manufacturer which helps the company competitively as more data equals better performing models especially in terms of object detection. More data from millions of cars on the roads also helps with prediction as Tesla collects data from incorrect predictions that can be added to the training set. By leveraging a prediction neural network, Tesla does not need to use human labeling or annotation and can instead use what’s called a temporal sequence of events — in other words, Tesla rewinds events and labels objects automatically with the use of a supercomputer.

The advantage here is that training neural networks correlates with the miles (which again, are substantial due to size of fleet on the road compared to competitors) rather than correlating with the need for human labeling. The result of automatic labeling is that Tesla is able to predict rare situations with more accuracy.

Where Nvidia delivers a strong advantage is the company has decades of history with graphics and simulation due to its gaming roots. As stated, Tesla also uses imitation learning and has a photorealistic simulator which uses vector space for labels and functions like a game engine. However, Nvidia has been quietly working on their simulation platform for many years internally despite only recently marketing Omniverse to the public. In this case, Nvidia has such a high-level of confidence in their simulation skills that they forego the real-life fleet to primarily train virtual 3D models. The company is also packaging the simulation platform for many other uses cases, such as AI factories, 5G networks, power plants and climate research. Developers can work with 3D tools through Python-based development.

Here’s a 10-minute demonstration with the simulation platform here around minute 7:00.

To keep it simple, Tesla’s primary advantage is the data they have collected as no other EV/AV has collected this level of data from real drivers. To contrast, Nvidia has arguably the best simulation platform due to decades of graphics work. These digital twins are only now being widely marketed despite being in development for over 5 years. The license costs $9,000 and Nvidia has estimated its current addressable market is 20 million engineers. Notably, Nvidia’s Hyperion will also be deployed in millions of vehicles over time, offering similar levels of data as Tesla’s fleet.

The Tesla VS Nvidia debates have not formally begun but they are certainly in our future … so brace yourself. Ultimately, the way Nvidia stands apart is the company does not directly compete on manufacturing vehicles. This is something anyone can agree on. That means many OEMs will use Nvidia’s DRIVE system whereas Tesla is less likely to commercialize their software as they’re viewed as a main competitor.

As long as Nvidia continues to innovate and maintain a lead, the popularity of its DRIVE system is likely to remain due to the company’s strategic advantages in AI and supercomputing. The company did an excellent job of tackling the edge computing use case of autonomous vehicles first.

Hardware is only part of the equation. The long-term plan is for Nvidia to license software for autonomous vehicles, which will create a recurring revenue stream. The licensing fees will go well beyond Omniverse to include the actual owner of the vehicle paying a subscription fee to Nvidia for its software. Tesla does this with their AutoPilot software which has grown from $5,000 to $12,000 per vehicle.

The breakdown according to the 2022 Investor Presentation looks like this:

  • $100 billion from gaming
  • $300 billion from chips and systems
  • $150 billion from AI Enterprise software
  • $150 billion from Omniverse software – fees are charged to both users and robots/digital twins
  • $300 billion from Automotive – primarily software

What Nvidia is communicating is that software revenue will surpass hardware revenue long-term.

Here is what Kress stated: "Our software content per vehicle can be in the thousands of dollars over the lifetime of the vehicle compared to the hundreds of dollars for the hardware. And second, software scales with the installed base of vehicles, not annual production.”

Note on CUDA:

The software discussion on Nvidia is not complete without a mention of CUDA. We called this Nvidia’s moat back in 2018 and we continue to believe it provides an important moat. The CUDA-related libraries include frameworks that span quantum computing, robotics, 5G networks, cybersecurity and drug discovery. The universal skills taught around CUDA and Nvidia’s SDKs help to drive more business for Nvidia’s GPUs.

Note: I’ve covered Omniverse in-depth here.

Risk: Valuation

The primary risk right now is valuation as Nvidia trades 2X higher than its peers on both the top line sales valuations and on the bottom line with earnings and cash-based valuations. There’s probably equal risk in waiting for Nvidia to drop another 50% as there is in buying Nvidia at the 2X valuation. One reason Nvidia may be valued here is because it’s slowly becoming a software company. Regardless, Knox’s technicals help immensely in determining if the market will continue to award Nvidia it’s gold medal valuation or if the market will discount Nvidia based on sentiment-driven headlines. This is a position we plan to keep on building so you can keep an eye out for those trade alerts over the next few years.

Conclusion:

Finding great companies is only half the battle, fighting negative sentiment is the other half – and semis have no shortage of this in any market – hence our beginning quotes from 2019 and also 2021.

Nvidia is the strongest company in terms of product on the market today. That doesn’t mean semis won’t be a roller coaster – we should fully expect that semis will undulate in sentiment and price while we hold our stocks over many years. We can’t change the way Wall Street works — which is a pendulum that swings between value stocks and growth stocks — but we can describe in great detail why concerns around gaming and consumer electronics slowing down is not going to change our position. We do not care to perfectly time entries or to find a perfect bottom – you’ll be hard pressed to find any legendary investor recommend that this be an investor’s goal. What we care about is finding quality companies and building those positions over time. Nvidia fits this description.