Chip comparison
Cambricon MLU370-X8 vs NVIDIA A100 Tensor Core GPU
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 | Cambricon MLU370-X8 | NVIDIA A100 Tensor Core GPU |
|---|---|---|
| Process node | TSMC N7 | TSMC N7 |
| Transistors (B) | — | 54 |
| Die size | — | 826 mm² |
| TDP | 250 W | 400 W |
| Memory | 48 GB LPDDR5 | 80 GB HBM2e |
| Memory bandwidth | — | 2,039 GB/s |
| FP16 (dense) | 96 TFLOPS | 312 TFLOPS |
| BF16 (dense) | — | 312 TFLOPS |
| INT8 (dense) | 256 TOPS | 624 TOPS |
| Launch price (list) | — | $10,000 |
| Form factor | PCIe | SXM4 |
| Announced | 2022-03-25 | 2020-05-14 |
| Released | — | 2020-05-14 |
Robots running Cambricon MLU370-X8
No robots publicly running it yet.
Robots running NVIDIA A100 Tensor Core GPU
No robots publicly running it yet.
Data centers with Cambricon MLU370-X8
No data centers publicly running it yet.
Data centers with NVIDIA A100 Tensor Core GPU
No data centers publicly running it yet.
Common questions
Cambricon MLU370-X8 vs NVIDIA A100 Tensor Core GPU: which is faster for training?
NVIDIA A100 Tensor Core GPU has 3.25x the dense FP16/BF16 throughput of the other (Cambricon MLU370-X8: 96 TFLOPS; NVIDIA A100 Tensor Core GPU: 312 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
Cambricon MLU370-X8 vs NVIDIA A100 Tensor Core GPU: which has more memory?
NVIDIA A100 Tensor Core GPU carries more HBM (Cambricon MLU370-X8: 48 GB LPDDR5; NVIDIA A100 Tensor Core GPU: 80 GB HBM2e). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
Cambricon MLU370-X8 vs NVIDIA A100 Tensor Core GPU: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Cambricon MLU370-X8 0.38 (96 ÷ 250 W); NVIDIA A100 Tensor Core GPU 0.78 (312 ÷ 400 W). NVIDIA A100 Tensor Core GPU wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · Cambricon MLU370-X8 full page · NVIDIA A100 Tensor Core GPU full page.