Layer 2 · head to head
AWS Trainium 3 vs AMD Instinct MI355X
The two most credible cost-per-token challengers, one locked to a cloud, one not.
| AWS Trainium 3 | AMD Instinct MI355X | |
|---|---|---|
| Can you get it | Cannot be bought. Exists only inside one cloud, so choosing it is choosing that cloud. | Rentable by the hour from multiple clouds; purchasable at scale. |
| Memory | 144 GB HBM3e per chip | 288 GB HBM3E |
| Bandwidth | 4.9 TB/s per chip | 8 TB/s |
| Compute | 2.52 PFLOPS FP8 per chip | ~10 PFLOPS sparse FP4 |
| Power | Not published | Not consistently published |
| Interconnect | NeuronLink | Infinity Fabric |
| Software | AWS Neuron SDK | ROCm |
| Workload | both | both |
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.
AMD Instinct MI355X
For: Memory-bound inference. 288 GB against Blackwell's 192 GB is the cleanest technical argument AMD has made in a decade.
The catch: ROCm, not CUDA. The gap has narrowed sharply but it is still the reason procurement teams say no, and porting cost is real.
Economics: AMD positions this as the best price-per-token part for inference at scale, and claims 2.6x H100 inference throughput on Llama 3.1 405B for the MI350X.
Before either — can you power it?
Not published against Not consistently 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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