Layer 2 · head to head
AWS Trainium 2 vs AWS Trainium 3
Region availability against raw capability. On AWS the older part is often the one you can actually get.
| AWS Trainium 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 | Below Trainium 3 | 2.52 PFLOPS FP8 per chip |
| Power | Not published | Not published |
| Interconnect | NeuronLink | NeuronLink |
| Software | AWS Neuron SDK | AWS Neuron SDK |
| Workload | both | both |
AWS Trainium 2
For: The generation currently at widest availability across AWS regions.
The catch: Same AWS lock-in, and region availability is often the real constraint rather than the chip.
Economics: Cheaper per hour than Trainium 3 and frequently the better value for inference that does not need the newer part.
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.