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Amazon's purpose-built training chip with the best price/performance on AWS.
Pros
- Strong price/performance vs NVIDIA on AWS
- Deep AWS ecosystem and EFA networking integration
- UltraServer scale-up to 64 chips
Cons
- AWS-only; not portable off the cloud
- Neuron SDK ecosystem narrower than CUDA
- Best results require code tuning for Neuron
✓ Where it shines / best for
- Cost-conscious large-model training and inference on AWS
- Teams already on AWS wanting a GPU alternative via EC2
- Scaling to UltraServer/UltraCluster for frontier models
✕ Not the best fit for
- On-premises deployments (AWS cloud only)
- Workloads that cannot adapt to the Neuron SDK toolchain
- Edge or on-device inference
Features
- ✓ AI inference
- ✓ Data-center scale
- ✓ LLM
- ✓ API access
- ✓ AI Training
- ✓ Cloud Only
- ✓ HBM3
- ✓ High Bandwidth
- ✓ Jax
- ✓ Neuron SDK
- ✓ Pytorch
Pricing
| Plan | Price | Billing | Notes |
|---|---|---|---|
| On-demand (cloud) | Usage-based | hourly | Accessed only via Amazon EC2 Trn2 instances (e.g., trn2.48xlarge with 16 Trainium2 chips); priced per instance-hour on AWS. Not sold as standalone silicon. See EC2 on-demand pricing page for current rates. |
| Savings Plans / Reserved | Discounted usage-based | 1 or 3 years | Lower effective hourly rate via EC2 Savings Plans or Reserved capacity commitments. |
| Spot | Discounted usage-based | hourly | Spot pricing available at a discount with interruption risk. |
Pricing verified from the official source. Prices change often — confirm on the vendor's site before buying.
Specifications
| memory | 1.5 TB HBM3 per instance, 46 TB/s |
| networking | 3.2 Tbps EFAv3 (instance) |
| architecture | Trainium2, AWS custom ASIC |
| fp8_performance | 20.8 PFLOPS (instance) / 83.2 PFLOPS (UltraServer) |
| chips_per_instance | 16 (Trn2) / 64 (UltraServer) |
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A full review is being generated for this product and will appear here shortly.