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Chip comparison

Cambricon MLU370-X8 vs NVIDIA V100 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.

FieldCambricon MLU370-X8NVIDIA V100 Tensor Core GPU
Process nodeTSMC N7TSMC 12FFN
Transistors (B)21.1
Die size815 mm²
TDP250 W300 W
Memory48 GB LPDDR532 GB HBM2
Memory bandwidth900 GB/s
FP16 (dense)96 TFLOPS125 TFLOPS
INT8 (dense)256 TOPS
Form factorPCIeSXM2 / PCIe
Announced2022-03-252017-05-10
Released2017-12-01

Robots running Cambricon MLU370-X8

No robots publicly running it yet.

Robots running NVIDIA V100 Tensor Core GPU

No robots publicly running it yet.

Data centers with Cambricon MLU370-X8

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Data centers with NVIDIA V100 Tensor Core GPU

No data centers publicly running it yet.

Common questions

Cambricon MLU370-X8 vs NVIDIA V100 Tensor Core GPU: which is faster for training?

NVIDIA V100 Tensor Core GPU has 1.30x the dense FP16/BF16 throughput of the other (Cambricon MLU370-X8: 96 TFLOPS; NVIDIA V100 Tensor Core GPU: 125 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 V100 Tensor Core GPU: which has more memory?

Cambricon MLU370-X8 carries more HBM (Cambricon MLU370-X8: 48 GB LPDDR5; NVIDIA V100 Tensor Core GPU: 32 GB HBM2). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.

Cambricon MLU370-X8 vs NVIDIA V100 Tensor Core GPU: which is more power-efficient?

Dense FP16/BF16 TFLOPS per watt: Cambricon MLU370-X8 0.38 (96 ÷ 250 W); NVIDIA V100 Tensor Core GPU 0.42 (125 ÷ 300 W). NVIDIA V100 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 V100 Tensor Core GPU full page.