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Amazon's 3nm training chip: 2.52 PFLOPS FP8, 144GB HBM3e
Pros
- Strong cost-per-token on AWS
- Proven at frontier-lab scale
- Deep AWS integration
- Big generational jump over Trainium2
Cons
- AWS-only
- Neuron SDK porting effort
- Not a CUDA drop-in
- Cloud-rental only
✓ Where it shines / best for
- Large-scale training on AWS
- Cost-optimized LLM workloads
- Anthropic/OpenAI-style scale
- AWS-native shops
✕ Not the best fit for
- Multi-cloud portability
- On-prem
- CUDA-locked code
- Edge
Features
- ✓ 3nm process
- ✓ 2.52 PFLOPS FP8 per chip
- ✓ 144GB HBM3e
- ✓ Trn3 UltraServer scale-out
- ✓ NeuronLink interconnect
- ✓ Neuron SDK
Pricing
| Plan | Price | Billing | Notes |
|---|---|---|---|
| Trn3 instances | Cloud rental (varies) | one-time | Consumption-based via EC2; not sold |
Pricing verified from the official source. Prices change often — confirm on the vendor's site before buying.
Specifications
| use | Cloud AI training and inference |
| power | Data center scale |
| memory | 144GB HBM3e |
| performance | 2.52 PFLOPS FP8 per chip |
| architecture | Trainium3 (3nm) |
Sponsored
A full review is being generated for this product and will appear here shortly.