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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-efficient LLM training/inference on AWS
- Teams standardized on SageMaker and EC2
- Large-batch training in UltraClusters
✕ Not the best fit for
- On-prem or multi-cloud deployments
- CUDA-locked codebases
- Latency-critical single-token inference niches
Features
- ✓ 16 Trainium2 chips per Trn2 instance via NeuronLink
- ✓ Up to 20.8 FP8 petaflops per instance
- ✓ 1.5 TB HBM3 with 46 TB/s memory bandwidth
- ✓ 3.2 Tbps EFAv3 networking
- ✓ Trn2 UltraServers scale to 64 chips (83.2 PFLOPS FP8)
- ✓ AWS Neuron SDK with PyTorch/JAX support
- ✓ EC2 UltraClusters for hyperscale training
- ✓ Tight integration with SageMaker and AWS stack
Pricing
| Plan | Price | Billing | Notes |
|---|---|---|---|
| trn2.48xlarge On-Demand | ~$4.80 (varies) | per hour | 16-chip instance; AWS does not publish a single headline rate (varies by region/availability). Third-party estimate ~$4.80/hr; some quoted ~$8.60/hr. See EC2 Trn2 pricing page. |
| Trn2 UltraServer | Varies | per hour | 64 Trainium2 chips, 83.2 PFLOPS FP8; via Capacity Blocks / contracts |
| Savings Plans / Reserved | Discounted | 1-3 year | Lower rates with commitments |
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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