DEPLOYDatabase

Chip comparison

Huawei Ascend 910D vs Tenstorrent Blackhole

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 910DTenstorrent Blackhole
Process nodeSMIC N+2 (~7nm)TSMC N6
TDP800 W300 W
Memory192 GB HBM332 GB GDDR6
Memory bandwidth4,000 GB/s512 GB/s
FP16 (dense)1,200 TFLOPS745 TFLOPS
BF16 (dense)1,200 TFLOPS
FP8 (dense)2,400 TFLOPS
INT8 (dense)2,980 TOPS
Form factorOAMPCIe
Announced2025-04-012024-07-01
Released2025-12-012025-01-01

Robots running Huawei Ascend 910D

No robots publicly running it yet.

Robots running Tenstorrent Blackhole

No robots publicly running it yet.

Data centers with Huawei Ascend 910D

No data centers publicly running it yet.

Data centers with Tenstorrent Blackhole

No data centers publicly running it yet.

Common questions

Huawei Ascend 910D vs Tenstorrent Blackhole: which is faster for training?

Huawei Ascend 910D has 3.22x the dense FP16/BF16 throughput of the other (Huawei Ascend 910D: 2,400 TFLOPS; Tenstorrent Blackhole: 745 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 Tenstorrent Blackhole: which has more memory?

Huawei Ascend 910D carries more HBM (Huawei Ascend 910D: 192 GB HBM3; Tenstorrent Blackhole: 32 GB GDDR6). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.

Huawei Ascend 910D vs Tenstorrent Blackhole: which is more power-efficient?

Dense FP16/BF16 TFLOPS per watt: Huawei Ascend 910D 3.00 (2,400 ÷ 800 W); Tenstorrent Blackhole 2.48 (745 ÷ 300 W). Huawei Ascend 910D wins at the die level; system-level efficiency also depends on cooling and interconnect.

See also: every chip comparison · Huawei Ascend 910D full page · Tenstorrent Blackhole full page.