AWS reports growing interest in its custom-designed Trainium and Inferentia chips as organizations look to cut costs and reduce their dependence on Nvidia GPUs. The numbers backing that interest are starting to look less like a side project and more like a real business.

Organizations migrating inference tasks from Nvidia GPUs to Inferentia instances are reporting cost reductions in the range of 80-90%. That’s not a marginal improvement. That’s the difference between a viable AI product and one that bleeds money at scale.

On the training side, Trainium chips are carving out their own niche. They lack the raw ecosystem depth of Nvidia’s CUDA platform, which remains the industry’s default software layer for GPU programming. But for organizations willing to optimize their workloads around Amazon’s hardware, the price-performance ratio is compelling enough to justify the switch.

Anthropic, the AI safety lab behind the Claude model family, operates a cluster of roughly 500,000 Trainium chips as part of a project called Rainier. That deployment reportedly delivers a fivefold increase in compute capability compared to the company’s previous AI models. Amazon has invested a total of $8 billion in Anthropic, designating AWS as its primary cloud and training partner.