Chip comparison
AWS Inferentia 2 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 Inferentia 2 | Cambricon MLU370-X8 |
|---|---|---|
| Process node | TSMC N7 | TSMC N7 |
| TDP | 250 W | 250 W |
| Memory | 32 GB HBM3 | 48 GB LPDDR5 |
| Memory bandwidth | 820 GB/s | — |
| FP16 (dense) | — | 96 TFLOPS |
| BF16 (dense) | 190 TFLOPS | — |
| FP8 (dense) | 380 TFLOPS | — |
| INT8 (dense) | — | 256 TOPS |
| Form factor | Inf2 EC2 instance | PCIe |
| Announced | 2022-12-01 | 2022-03-25 |
| Released | 2023-04-01 | — |
Robots running AWS Inferentia 2
No robots publicly running it yet.
Robots running Cambricon MLU370-X8
No robots publicly running it yet.
Data centers with AWS Inferentia 2
No data centers publicly running it yet.
Data centers with Cambricon MLU370-X8
No data centers publicly running it yet.
Common questions
AWS Inferentia 2 vs Cambricon MLU370-X8: which is faster for training?
AWS Inferentia 2 has 3.96x the dense FP16/BF16 throughput of the other (AWS Inferentia 2: 380 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 Inferentia 2 vs Cambricon MLU370-X8: which has more memory?
Cambricon MLU370-X8 carries more HBM (AWS Inferentia 2: 32 GB HBM3; 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 Inferentia 2 vs Cambricon MLU370-X8: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: AWS Inferentia 2 1.52 (380 ÷ 250 W); Cambricon MLU370-X8 0.38 (96 ÷ 250 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 · Cambricon MLU370-X8 full page.