Intel Might Be Quitting the AI Training Market for Good

Intel (INTC -1.63%) has already pulled back on its effort to directly compete with Nvidia in the AI accelerator market. The company's Gaudi line of AI chips held promise, but immature software and an unfamiliar architecture ultimately doomed Intel's flagship AI offerings. The company later axed Falcon Shores, which was meant to succeed Gaudi 3 as a more traditional GPU, instead shifting its focus to rack-level solutions.

Reporting from The Oregonian suggests that Intel may now be pulling back further. In a recent broadcast to employees, CEO Lip-Bu Tan laid out some hard truths as the company embarks on a turnaround plan. One statement seems to put a nail in the coffin for Intel's AI chip efforts: "On training I think it is too late for us." Tan noted that Nvidia's market position was too strong to catch up.

AI is still an opportunity for Intel

There are two distinct markets for AI accelerators. First, there's AI training, which uses powerful GPUs and mountains of data to train AI models in a computationally intensive process. The Grok 3 AI model from xAI used a cluster of 100,000 Nvidia H100 GPUs for training. Nvidia dominates the market for AI training chips, in part because its data center GPUs are the most powerful available.

Second, there's AI inference. Once an AI model has been trained, inference is the process of using that trained model. For the most advanced AI models running in cloud data centers, inference still requires extremely powerful GPUs with lots of ultra-fast memory. For smaller AI models, less powerful hardware can be more than sufficient.

Tan is right: At this point, there's probably no chance Intel will catch up in the AI training market. However, AI inference could ultimately be an even larger opportunity. Cloudflare, a leading edge-computing provider, has been predicting that inference would be a larger market than training in the long run. Cloudflare offers a variety of smaller AI models through its platform, and it can get away with using older, less powerful AI accelerators while still providing acceptable response times for its users.

AI inference, as well as agentic AI, will be Intel's focus from here on out, according to Tan. Speaking about agentic AI, Tan said: "That's an area that I think is emerging, coming up very big and we want to make sure that we capture." AI chips in edge data centers and directly inside devices like PCs designed to run fully trained AI models are areas where Intel could still win.

Some companies are pushing toward smaller, more efficient AI models capable of being run on cheaper hardware. IBM recently previewed its Granite 4.0 Tiny AI model, which will be capable of running on consumer-grade GPUs that cost just a few hundred dollars rather than data center GPUs that can cost tens of thousands of dollars. Selling AI chips that can run these types of models efficiently could be a huge market opportunity for Intel.

Unanswered questions

One big unknown right now is whether Intel will continue developing rack-scale AI solutions. The company previously stated that it would focus on Jaguar Shores, originally meant to succeed the now-defunct Falcon Shores, in the context of a rack-scale AI solution. It's unclear if Tan's statements mean that Intel is giving up on Jaguar Shores, or if Tan still sees rack-scale AI solutions as a viable market for the company.

Either way, it seems that Intel is refocusing its AI efforts on inference and largely ceding the AI training market to Nvidia and AMD. Given where Intel is today, this new strategy makes sense. However, as Tan noted in his communication with employees, a turnaround for Intel is going to be a "marathon."