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Chip comparison

Huawei Ascend 910D vs NVIDIA B100 (Blackwell)

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.

FieldHuawei Ascend 910DNVIDIA B100 (Blackwell)
Process nodeSMIC N+2 (~7nm)TSMC 4NP
TDP800 W700 W
Memory192 GB HBM3192 GB HBM3e
Memory bandwidth4,000 GB/s8,000 GB/s
FP16 (dense)1,200 TFLOPS1,750 TFLOPS
BF16 (dense)1,200 TFLOPS1,750 TFLOPS
FP8 (dense)2,400 TFLOPS3,500 TFLOPS
Form factorOAMSXM6 (700 W envelope)
Announced2025-04-012024-03-18
Released2025-12-012024-11-01

Robots running Huawei Ascend 910D

No robots publicly running it yet.

Robots running NVIDIA B100 (Blackwell)

No robots publicly running it yet.

Data centers with Huawei Ascend 910D

No data centers publicly running it yet.

Data centers with NVIDIA B100 (Blackwell)

No data centers publicly running it yet.

Common questions

Huawei Ascend 910D vs NVIDIA B100 (Blackwell): which is faster for training?

NVIDIA B100 (Blackwell) has 1.46x the dense FP8 throughput of the other (Huawei Ascend 910D: 2,400 TFLOPS; NVIDIA B100 (Blackwell): 3,500 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.

Huawei Ascend 910D vs NVIDIA B100 (Blackwell): which has more memory?

NVIDIA B100 (Blackwell) carries more HBM (Huawei Ascend 910D: 192 GB HBM3; NVIDIA B100 (Blackwell): 192 GB HBM3e). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.

Huawei Ascend 910D vs NVIDIA B100 (Blackwell): which is more power-efficient?

Dense FP8 TFLOPS per watt: Huawei Ascend 910D 3.00 (2,400 ÷ 800 W); NVIDIA B100 (Blackwell) 5.00 (3,500 ÷ 700 W). NVIDIA B100 (Blackwell) wins at the die level; system-level efficiency also depends on cooling and interconnect.

See also: every chip comparison · Huawei Ascend 910D full page · NVIDIA B100 (Blackwell) full page.