The AI Infrastructure Market Is Shifting to Inference. 5 Stocks to Play This Future $1.3 Trillion Market Opportunity
Geoffrey Seiler, The Motley Fool
Sat, August 1, 2026 at 3:20 PM GMT+3 4 min read
A major shift is underway in the AI infrastructure market, with inference becoming the primary growth driver, replacing AI model training. Bloomberg Intelligence projects that the inference market will grow at a 32% annual compound rate through 2032, reaching $1.3 trillion. That is about double the $658 billion it projects the AI training market will reach over the same period.
With inference spending expected to race higher and the semiconductor segment selling off, let's look at five AI stocks to play the growth in inference.
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1. Nvidia
When it comes to inference, Nvidia (NASDAQ: NVDA) doesn't have the same advantages it has in AI model training, where it is the undisputed leader. However, it would be foolish to count it out.
The company made a very smart "acquisition" this year by bringing Groq and its language processing units (LPUs) into the fold. LPUs have on-chip SRAM (static random-access memory) built into them. This helps reduce latency and makes them great chips for handling the decode phase of inference, when AI models provide answers to queries.
Meanwhile, its graphics processing units (GPUs), packaged with high-bandwidth memory (HBM), can do the heavy lifting during the pre-fill phase when the model reads a query. It's an eloquent solution that should make Nvidia an important player in the inference space moving forward.
2. Advanced Micro Devices
While AI training is all about raw compute power, inference tends to be more memory-constrained. That's why Advanced Micro Devices (NASDAQ: AMD) holds a much stronger position in the inference market than it does with training. It deploys a chiplet design, in which its GPUs act as part of a larger interconnected system and can be packaged with more memory.
The company already has large-scale inference deals with OpenAI, Meta Platforms, Microsoft, and Anthropic, which should drive strong growth in the coming years. It also just formed a partnership with the next company on this list, Cerebras (NASDAQ: CBRS), incorporating its SRAM-based solution into its new Helios racks to improve decode performance.
3. Cerebras
Cerebras uses chips with SRAM embedded directly on them, similar to Nvidia's LPUs. However, SRAM is bulky, so instead of using a small amount of SRAM like Nvidia does with its LPUs, Cerebras produces massive wafer-sized chips. Given their size, they only come as part of a complete system, since they have special cooling and power management requirements.
Originally a premium solution with a high price tag, Cerebras' CS system is starting to break into the mainstream with deals with OpenAI and others. Meanwhile, its partnership with AMD will help it adopt a lower-cost system, with AMD's GPUs handling the pre-fill inference phase more cost-effectively.
4. Broadcom
Running AI workloads is not cheap, and with inference an ongoing cost, more hyperscalers are turning to designing custom AI chips for this task. This is where Broadcom (NASDAQ: AVGO) steps in. It helps turn AI chip designs into physical chips that can be produced at scale and packaged with HBM.
The company helped Alphabet develop its well-regarded Tensor Processing Units (TPUs), and it is currently riding the TPU wave. At the same time, other hyperscalers have also turned to it to help them develop their own custom AI chips. The company sees this as a more than $100 billion business in fiscal 2027, with strong growth prospects thereafter.
5. SK Hynix
With inference being memory-bound, demand for HBM is surging. SK Hynix (NASDAQ: SKHY) is the leader in this field, having created this specialized form of DRAM (dynamic random access memory). It holds a nearly 60% share of the HBM market and is the leading supplier to Nvidia. It also just signed a huge $500 billion deal with the GPU giant.
While the memory market has historically been highly cyclical, strong demand from the AI infrastructure build-out and new long-term supply deals should position SK Hynix as an inference winner.
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Geoffrey Seiler has positions in Advanced Micro Devices, Alphabet, Broadcom, and Meta Platforms. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Broadcom, Meta Platforms, Microsoft, and Nvidia. The Motley Fool has a disclosure policy.
The AI Infrastructure Market Is Shifting to Inference. 5 Stocks to Play This Future $1.3 Trillion Market Opportunity was originally published by The Motley Fool
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