Layer 2 · head to head
AWS Inferentia 2 vs AWS Trainium 3
Inference-only and cheap against dense-training-capable. Model support decides this, not FLOPS.
| AWS Inferentia 2 | AWS Trainium 3 | |
|---|---|---|
| Can you get it | Cannot be bought. Exists only inside one cloud, so choosing it is choosing that cloud. | Cannot be bought. Exists only inside one cloud, so choosing it is choosing that cloud. |
| Memory | Not published in directly comparable terms | 144 GB HBM3e per chip |
| Bandwidth | Not published | 4.9 TB/s per chip |
| Compute | Inference-optimised | 2.52 PFLOPS FP8 per chip |
| Power | Not published | Not published |
| Interconnect | NeuronLink | NeuronLink |
| Software | AWS Neuron SDK | AWS Neuron SDK |
| Workload | inference | both |
AWS Inferentia 2
For: Cost-optimised inference on AWS for models the Neuron SDK supports well.
The catch: Model coverage is narrower than a GPU. Verify your exact model and quantisation compiles before you plan around the price.
Economics: The cheapest AWS inference silicon when your model is supported. Worth nothing at all when it is not.
AWS Trainium 3
For: Dense training and inference on AWS. Unlike TPU v7 it is designed for both, which makes it the more honest Trainium-vs-TPU comparison.
The catch: AWS-only, and the Neuron SDK is a third software ecosystem to support alongside CUDA and ROCm. Model coverage is the question to ask, not FLOPS.
Economics: Independent analysis has singled out Trainium as the NVIDIA alternative that actually pays off on cost per token — largely because AWS prices it to move.
Before either — can you power it?
Not published against Not published. In 2026 that comparison usually matters more than the FLOPS one: the US interconnection queue exceeds 2,600 GW with waits approaching five years, and roughly 80% of projects withdraw before energising.
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