Chip comparison
Huawei Ascend 910C vs Tesla Dojo D1
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 | Huawei Ascend 910C | Tesla Dojo D1 |
|---|---|---|
| Process node | SMIC N+2 (~7nm) | TSMC N7 |
| Transistors (B) | — | 50 |
| Die size | — | 645 mm² |
| TDP | 500 W | 400 W |
| Memory | 128 GB HBM2e | — |
| Memory bandwidth | 3,200 GB/s | — |
| FP16 (dense) | 800 TFLOPS | — |
| BF16 (dense) | 800 TFLOPS | 362 TFLOPS |
| FP8 (dense) | — | 362 TFLOPS |
| INT8 (dense) | 1,600 TOPS | — |
| Form factor | OAM | Training tile (25 D1 chips per tile) |
| Announced | 2024-09-19 | 2021-08-19 |
| Released | 2025-01-01 | 2023-07-01 |
Robots running Huawei Ascend 910C
No robots publicly running it yet.
Robots running Tesla Dojo D1
No robots publicly running it yet.
Data centers with Huawei Ascend 910C
No data centers publicly running it yet.
Data centers with Tesla Dojo D1
No data centers publicly running it yet.
Common questions
Huawei Ascend 910C vs Tesla Dojo D1: which is faster for training?
Huawei Ascend 910C has 2.21x the dense FP16/BF16 throughput of the other (Huawei Ascend 910C: 800 TFLOPS; Tesla Dojo D1: 362 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
Huawei Ascend 910C vs Tesla Dojo D1: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Huawei Ascend 910C 1.60 (800 ÷ 500 W); Tesla Dojo D1 0.91 (362 ÷ 400 W). Huawei Ascend 910C wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · Huawei Ascend 910C full page · Tesla Dojo D1 full page.