Chip comparison
AWS Inferentia 2 vs AWS Trainium (Trn1)
Side-by-side specs
Straight from each vendor's datasheet. Winner in bold: higher throughput, memory, or transistor count; lower power draw or price. Sparse (2:4) throughput roughly doubles the FP8/INT8 numbers shown.
| Field | AWS Inferentia 2 | AWS Trainium (Trn1) |
|---|---|---|
| Process node | TSMC N7 | TSMC N7 |
| TDP | 250 W | 500 W |
| Memory | 32 GB HBM3 | 32 GB HBM2e |
| Memory bandwidth | 820 GB/s | 820 GB/s |
| BF16 (dense) | 190 TFLOPS | 210 TFLOPS |
| FP8 (dense) | 380 TFLOPS | — |
| Form factor | Inf2 EC2 instance | Trn1 EC2 instance |
| Announced | 2022-12-01 | 2020-12-01 |
| Released | 2023-04-01 | 2022-10-01 |
Robots running AWS Inferentia 2
No robots publicly running it yet.
Robots running AWS Trainium (Trn1)
No robots publicly running it yet.
Data centers with AWS Inferentia 2
No data centers publicly running it yet.
Data centers with AWS Trainium (Trn1)
No data centers publicly running it yet.
Common questions
AWS Inferentia 2 vs AWS Trainium (Trn1): which is faster for training?
AWS Inferentia 2 has 1.81x the dense FP16/BF16 throughput of the other (AWS Inferentia 2: 380 TFLOPS; AWS Trainium (Trn1): 210 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
AWS Inferentia 2 vs AWS Trainium (Trn1): which has more memory?
AWS Trainium (Trn1) carries more HBM (AWS Inferentia 2: 32 GB HBM3; AWS Trainium (Trn1): 32 GB HBM2e). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
AWS Inferentia 2 vs AWS Trainium (Trn1): which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: AWS Inferentia 2 1.52 (380 ÷ 250 W); AWS Trainium (Trn1) 0.42 (210 ÷ 500 W). AWS Inferentia 2 wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · AWS Inferentia 2 full page · AWS Trainium (Trn1) full page.