Key Points
Cerebras and Nvidia are both using SRAM in their inference chips.
However, Cerebras is making massive-sized chips, while Nvidia has incorporated normal-sized LPUs into its chip ecosystem.
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While large language model (LLM) training dominated the first phase of artificial intelligence (AI), inference is eventually expected to become the much larger market.
While LLM training is compute-heavy and more technically challenging, inference tends to be memory-centric and needs to be more cost-efficient given that it's an ongoing process. Traditionally, graphics processing units (GPUs) and other AI accelerators are packaged with high-bandwidth memory (HBM) to help optimize their performance in this area.
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However, Nvidia (NASDAQ: NVDA), through its recent "acquisition" of Groq, and Cerebras Systems (NASDAQ: CBRS) are now looking toward on-chip SRAM (static random-access memory) to speed up AI workloads for inference. This is a new approach, and both companies are using SRAM in a much different way. While using SRAM can dramatically increase inference speeds, it is physically bulky, which creates some trade-offs between chip size, memory capacity, and the data center infrastructure required to power and cool the chips.
Let's look at the two approaches and see which semiconductor stock looks better positioned to become the inference market leader.
Image source: The Motley Fool.
Cerebras: Is bigger better?
To deal with the physical bulkiness of SRAM, Cerebras creates massive wafer-sized chips that can fit both a large amount of computing power and SRAM onto a single chip. However, this comes with additional issues that need to be addressed.
The first is that the chip manufacturing process is complex, and defects are common. The reason Taiwan Semiconductor Manufacturing has become a virtual monopoly in advanced chip manufacturing is that it can produce advanced chips at high yields, but even its goal for its newest technology is a yield of around 80%. When you're looking at very expensive, wafer-sized chips, though, that type of yield doesn't cut it. To address this issue, Cerebras adds extra cores to help it work around any defects to its chips.
In addition, its chips need special cooling and power management, which is why it doesn't sell them individually, instead only selling or renting them as part of its complete end-to-end server rack CS-3 system. While the company boasts that its systems can perform inference 15 times faster than a GPU, everything involved leads to a very expensive premium solution.
Nvidia: The ecosystem advantage
With its $20 billion "acquisition" of Groq, Nvidia gained access to the company's language processing units (LPUs) designed for inference. While LPUs also use SRAM, they are ordinary-sized chips. The trade-off is that LPUs use a very small amount of SRAM on each chip, so they have to be interconnected with other LPUs in a massive, complex cluster. This reduces efficiency.
By comparison, Cerebras' chips are six times faster. They also tend to be very inflexible and can only be used for inference.
However, the one big benefit of the Nvidia deal is that it has incorporated LPUs into its CUDA software platform and designed complete rack systems using both its GPUs and LPUs specifically for inference. GPUs packaged with HBM can handle the prefill phase of understanding a user's prompt, while LPUs can then take over the decode phase of providing the response. Because LPUs use SRAM memory, they can respond with almost no lag.
The better stock to own
Cerebras has an opportunity to turn the inference market on its head and has a large commitment from OpenAI that will fuel huge growth. However, the stock is trading at a huge valuation right out of the gate (more than 100 times trailing sales) and needs to prove it can become more than a niche player.
Nvidia, on the other hand, is already the well-established leader in LLM training. Its "acquisition" of Groq, meanwhile, looks like a great move that should help it become an important player in the inference market. By being able to combine its GPUs with LPUs in the same server, the company has found a way to take a niche product and bring it to the mainstream. As such, I think Nvidia is the better buy of the two stocks.
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Geoffrey Seiler has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Nvidia and Taiwan Semiconductor Manufacturing. The Motley Fool has a disclosure policy.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
