Mingxin FX vs Huawei OceanStor A Series (UCM): How to Choose
Huawei's A series + UCM is deeply integrated with the Ascend ecosystem and backed by a mature delivery system. Mingxin FX differs in cross-platform measurements (AMD/Ascend/MetaX), fully reproducible and verifiable numbers, and open pricing. Choose by platform and verification needs.
Stated honestly: Huawei's strengths
Huawei OceanStor A series targets AI scenarios; UCM (inference memory-data management) has been announced as open source and integrates deeply with the Ascend hardware/software stack. Huawei has a nationwide service network, a mature supply chain and large-scale delivery references. For all-Huawei-stack customers (Ascend + MindSpore/CANN), the A series is the natural choice (public-source basis).
Mingxin FX's differentiation
Platform neutrality: measurements span AMD MI308X (R1–R4), Ascend 910B (R9) and MetaX N260 (R5); the software stack builds on open-source vLLM/LMCache with no single-ecosystem lock-in.
Verifiability: throughput +29–40%, TTFT −26–32% and every other key figure come from signed third-party reports, with a code export package (R8) enabling independent reproduction — you can joint-test with your own workload before buying, and the G3 gate stops the engagement if unmet.
Price transparency: the FX100 fully-populated reference price of ¥371,200 (≈ ¥2,014/TB) is published on this site with a reproducible calculation (gongsi/make_quote_v2.py).
Selection advice
All-Ascend stack valuing first-party integrated service: evaluate Huawei's A series first. Mixed compute (AMD/MetaX/Hygon/NVIDIA installed base), reproducible-verification requirements, or budget sensitivity: put Mingxin FX into a joint-test comparison. Customers testing both side by side are equally welcome — gate-based acceptance is fair to everyone.
FAQ
UCM is open source — why buy a commercial solution?
An open-source framework covers the software layer; you still need high-performance storage hardware and engineering adaptation. Mingxin also builds on an open stack (LMCache) — the value is the complete delivery of hardware + adaptation + reproducible measurements.
Has Mingxin measured on Ascend?
Yes: R9 on the Atlas 910B ×8 platform measured model loading 6.2–9.3× plus training checkpoint and data-read acceleration (vs an NFS baseline); the report is available on request.
How do we run a fair comparison test?
Same workload, same network, same acceptance criteria. Mingxin's G1–G4 gate process welcomes side-by-side testing against any vendor's solution, with all metrics agreed in writing beforehand.
Data sources (verifiable)
Related reading
- Mingxin FX vs VAST Data: Two Routes in AI Storage
- KV Cache Offloading: Putting the Inference Cache on an All-Flash Array
- AI Inference Storage Acceleration: The Third Way to Raise Cluster Output Without Adding GPUs
Joint test first, decisions second: gate-based acceptance with built-in stop-loss
The full costing model is provided as reproducible Python after NDA — customers can rerun it with their own parameters. Every key figure on this site carries a report ID and is open to third-party verification.
This site presents business-cooperation information and constitutes neither an investment offer nor any promise of returns. Measured data come from signed / official test reports (see the Evidence Library); vendor specs, public sources and estimates are labeled as such.