Chip comparison
AWS Trainium (Trn1) vs Google TPU v5e
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 Trainium (Trn1) | Google TPU v5e |
|---|---|---|
| Process node | TSMC N7 | — |
| TDP | 500 W | 170 W |
| Memory | 32 GB HBM2e | 16 GB HBM2 |
| Memory bandwidth | 820 GB/s | 819 GB/s |
| BF16 (dense) | 210 TFLOPS | 197 TFLOPS |
| INT8 (dense) | — | 393 TOPS |
| Form factor | Trn1 EC2 instance | OAM (v5e pod, up to 256 chips) |
| Announced | 2020-12-01 | 2023-08-29 |
| Released | 2022-10-01 | 2023-11-01 |
Robots running AWS Trainium (Trn1)
No robots publicly running it yet.
Robots running Google TPU v5e
No robots publicly running it yet.
Data centers with AWS Trainium (Trn1)
No data centers publicly running it yet.
Data centers with Google TPU v5e
Common questions
AWS Trainium (Trn1) vs Google TPU v5e: which is faster for training?
AWS Trainium (Trn1) has 1.07x the dense FP16/BF16 throughput of the other (AWS Trainium (Trn1): 210 TFLOPS; Google TPU v5e: 197 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
AWS Trainium (Trn1) vs Google TPU v5e: which has more memory?
AWS Trainium (Trn1) carries more HBM (AWS Trainium (Trn1): 32 GB HBM2e; Google TPU v5e: 16 GB HBM2). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
AWS Trainium (Trn1) vs Google TPU v5e: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: AWS Trainium (Trn1) 0.42 (210 ÷ 500 W); Google TPU v5e 1.16 (197 ÷ 170 W). Google TPU v5e wins at the die level; system-level efficiency also depends on cooling and interconnect.
AWS Trainium (Trn1) vs Google TPU v5e: which is more widely used?
AWS Trainium (Trn1): 0 robots and 0 data centers publicly running it. Google TPU v5e: 0 robots and 1 data center. Empty here means no operator has said publicly, not zero adoption in the market.
See also: every chip comparison · AWS Trainium (Trn1) full page · Google TPU v5e full page.