Chip comparison
AWS Inferentia 2 vs Huawei Ascend 910D
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 | AWS Inferentia 2 | Huawei Ascend 910D |
|---|---|---|
| Process node | TSMC N7 | SMIC N+2 (~7nm) |
| TDP | 250 W | 800 W |
| Memory | 32 GB HBM3 | 192 GB HBM3 |
| Memory bandwidth | 820 GB/s | 4,000 GB/s |
| FP16 (dense) | — | 1,200 TFLOPS |
| BF16 (dense) | 190 TFLOPS | 1,200 TFLOPS |
| FP8 (dense) | 380 TFLOPS | 2,400 TFLOPS |
| Form factor | Inf2 EC2 instance | OAM |
| Announced | 2022-12-01 | 2025-04-01 |
| Released | 2023-04-01 | 2025-12-01 |
Robots running AWS Inferentia 2
No robots publicly running it yet.
Robots running Huawei Ascend 910D
No robots publicly running it yet.
Data centers with AWS Inferentia 2
No data centers publicly running it yet.
Data centers with Huawei Ascend 910D
No data centers publicly running it yet.
Common questions
AWS Inferentia 2 vs Huawei Ascend 910D: which is faster for training?
Huawei Ascend 910D has 6.32x the dense FP8 throughput of the other (AWS Inferentia 2: 380 TFLOPS; Huawei Ascend 910D: 2,400 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
AWS Inferentia 2 vs Huawei Ascend 910D: which has more memory?
Huawei Ascend 910D carries more HBM (AWS Inferentia 2: 32 GB HBM3; Huawei Ascend 910D: 192 GB HBM3). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
AWS Inferentia 2 vs Huawei Ascend 910D: which is more power-efficient?
Dense FP8 TFLOPS per watt: AWS Inferentia 2 1.52 (380 ÷ 250 W); Huawei Ascend 910D 3.00 (2,400 ÷ 800 W). Huawei Ascend 910D wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · AWS Inferentia 2 full page · Huawei Ascend 910D full page.