Huawei Ascend 910C
Domestic training and inference silicon with the CANN/MindSpore stack and CloudMatrix racks.
Full Huawei Ascend 910C record →Head to head
Huawei Ascend 910C vs Google TPU Ironwood
Huawei Ascend 910C (Huawei) and Google TPU Ironwood (Google) both sit in Chips & Compute. Google TPU Ironwood shipped more tracked changes in the last 30 days (11 vs 3). Every value below comes from the latest crawl of the vendors' own pages.
| Attribute | Huawei Ascend 910C Huawei | Google TPU Ironwood |
|---|---|---|
| Segment | Chips & Compute | Chips & Compute |
| Version | Ascend 910C | 7th-generation (Ironwood) |
| Entry cost | Pricing is not publicly available; enterprise/quote-based pricing. | from $5.4/unit |
| Pricing tiers | — | On Demand (us-central1) $12 · DWS Flex-start (us-central1) $6 · DWS Calendar Mode (us-central1) $8.4 · 1-year Commitment (us-central1) $8.4 · 3-year Commitment (us-central1) $5.4 · On Demand (europe-west2) $13.2 · DWS Flex-start (europe-west2) $6 · DWS Calendar Mode (europe-west2) $8.4 |
| Model stack | Ascend 910C · CANN / MindSpore | Gemini · Google TPU Ironwood · Ironwood · Ironwood (TPU v7) · Ironwood TPU · Ironwood TPU (7th Generation) · TPU v7 · TPU7x · TPU7x (Ironwood) · XLA compiler |
| Context window | — | — |
| Public API | ||
| Routes models | ||
| Changes / 30d | 3 | 11 |
| Origin | China | Global |
| Capabilities | dual-die 910C, CloudMatrix 384 racks, CANN toolkit, MindSpore, CANN 异构计算架构, MindSpore AI框架, MindStudio 全流程工具链, NPU | 9,216-chip pods, inference optimised, liquid cooled, JAX + PyTorch/XLA, Large-scale training, Reasoning and inference, 9,216 liquid-cooled chips per pod, 42.5 ExaFlops performance |
Google TPU Ironwood
v7 TPU pods scaling to 9,216 chips, available through Google Cloud and used for Gemini.
Full Google TPU Ironwood record →