Chip comparison
AWS Trainium (Trn1) vs Cambricon MLU370-X8
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) | Cambricon MLU370-X8 |
|---|---|---|
| Process node | TSMC N7 | TSMC N7 |
| TDP | 500 W | 250 W |
| Memory | 32 GB HBM2e | 48 GB LPDDR5 |
| Memory bandwidth | 820 GB/s | — |
| FP16 (dense) | — | 96 TFLOPS |
| BF16 (dense) | 210 TFLOPS | — |
| INT8 (dense) | — | 256 TOPS |
| Form factor | Trn1 EC2 instance | PCIe |
| Announced | 2020-12-01 | 2022-03-25 |
| Released | 2022-10-01 | — |
Robots running AWS Trainium (Trn1)
No robots publicly running it yet.
Robots running Cambricon MLU370-X8
No robots publicly running it yet.
Data centers with AWS Trainium (Trn1)
No data centers publicly running it yet.
Data centers with Cambricon MLU370-X8
No data centers publicly running it yet.
Common questions
AWS Trainium (Trn1) vs Cambricon MLU370-X8: which is faster for training?
AWS Trainium (Trn1) has 2.19x the dense FP16/BF16 throughput of the other (AWS Trainium (Trn1): 210 TFLOPS; Cambricon MLU370-X8: 96 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 Cambricon MLU370-X8: which has more memory?
Cambricon MLU370-X8 carries more HBM (AWS Trainium (Trn1): 32 GB HBM2e; Cambricon MLU370-X8: 48 GB LPDDR5). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
AWS Trainium (Trn1) vs Cambricon MLU370-X8: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: AWS Trainium (Trn1) 0.42 (210 ÷ 500 W); Cambricon MLU370-X8 0.38 (96 ÷ 250 W). AWS Trainium (Trn1) wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · AWS Trainium (Trn1) full page · Cambricon MLU370-X8 full page.