Chip comparison
Cerebras WSE-3 (Wafer-Scale Engine 3) 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 | Cerebras WSE-3 (Wafer-Scale Engine 3) | Huawei Ascend 910D |
|---|---|---|
| Process node | TSMC N5 | SMIC N+2 (~7nm) |
| Transistors (B) | 4000 | — |
| Die size | 46225 mm² | — |
| TDP | 23000 W | 800 W |
| Memory | 44 GB on-die SRAM | 192 GB HBM3 |
| Memory bandwidth | 21,000,000 GB/s | 4,000 GB/s |
| FP16 (dense) | 62,500 TFLOPS | 1,200 TFLOPS |
| BF16 (dense) | — | 1,200 TFLOPS |
| FP8 (dense) | — | 2,400 TFLOPS |
| Form factor | wafer-scale (single-wafer system) | OAM |
| Announced | 2024-03-13 | 2025-04-01 |
| Released | 2024-04-01 | 2025-12-01 |
Robots running Cerebras WSE-3 (Wafer-Scale Engine 3)
No robots publicly running it yet.
Robots running Huawei Ascend 910D
No robots publicly running it yet.
Data centers with Cerebras WSE-3 (Wafer-Scale Engine 3)
No data centers publicly running it yet.
Data centers with Huawei Ascend 910D
No data centers publicly running it yet.
Common questions
Cerebras WSE-3 (Wafer-Scale Engine 3) vs Huawei Ascend 910D: which is faster for training?
Cerebras WSE-3 (Wafer-Scale Engine 3) has 26.04x the dense FP16/BF16 throughput of the other (Cerebras WSE-3 (Wafer-Scale Engine 3): 62,500 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.
Cerebras WSE-3 (Wafer-Scale Engine 3) vs Huawei Ascend 910D: which has more memory?
Huawei Ascend 910D carries more HBM (Cerebras WSE-3 (Wafer-Scale Engine 3): 44 GB on-die SRAM; Huawei Ascend 910D: 192 GB HBM3). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
Cerebras WSE-3 (Wafer-Scale Engine 3) vs Huawei Ascend 910D: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Cerebras WSE-3 (Wafer-Scale Engine 3) 2.72 (62,500 ÷ 23000 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 · Cerebras WSE-3 (Wafer-Scale Engine 3) full page · Huawei Ascend 910D full page.