6.2–9.3×
Faster model loading
vs. NFS, on Huawei Ascend 910B
On Huawei Atlas 910B: DeepSeek-32B serving load 691 s → 112 s (6.2×); DeepSeek-70B 1399 s → 150 s (9.3×).
MeasuredSourceR9
Report R9 · Benchmark report
FX100 on Huawei Ascend 910B: Inference and Training Benchmark (vs. NFS Baseline)
Three comparison groups against an NFS baseline on an Ascend platform with 8 × Huawei Atlas 910B: model loading for inference (DeepSeek-32B / 70B), training weight and checkpoint I/O (Qwen-7B), and training-data acceleration (YOLOv8 / COCO). The platform is Ascend 910B, not MI308X, and is labeled as such wherever this site quotes it.
- Date
- Type
- Benchmark report
- Test platform
- 8 × Huawei Atlas 910B (Kunpeng-920)
Key findings
- DeepSeek-32B serving load: 691 s → 112 s (6.2×)
- DeepSeek-70B serving load: 1399 s → 150 s (9.3×)
- Also covers training weight / checkpoint I/O and training-data acceleration
Figures this report supports
Pages that cite this report
- Product: FX100
The workhorse with the most complete benchmark record
- Solution: Storage Acceleration: KV Cache Tiering
Storage acceleration
- Solution: Enablement for Domestic and Non-NVIDIA Accelerators
Accelerator enablement
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Test jointly first, decide afterwards
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