Amazon’s Trainium Takes a Strike at the Training King

Trainium has already proven itself in the realm of inference. Anthropic is running production inference and RL rollouts on Trainium2 and Trainium3. Historically, Trainium has been great for inference but not the best fit for research training. In inference, the model architecture changes at a much slower pace compared to training, where researchers iterate on many different experiments and ablations of model architecture and need a stack that covers a broad API surface. In the past, that is where Trainium fell short.

The AWS Neuron team is aggressively working to change this pitfall with the biggest shift being moving towards a first-class, native PyTorch training experience. Gone are the days of sprinkling xm.step() through your training loop and wrestling with a bespoke XLA-style execution model. Trainium now runs PyTorch the way PyTorch users expect, in eager mode, with torch.compile and the standard APIs.

The SemiAnalysis engineering team has been trying out the Trainium3 racks…