Mingxin FX vs VAST Data: Two Routes in AI Storage
VAST is a global leader in AI storage, with the DASE architecture and deep NVIDIA-ecosystem alignment. Mingxin FX goes deep on the single KV-cache-tiering scenario, with strengths in domestic-platform adaptation, reproducible measurements and China delivery cost.
Stated honestly: VAST's strengths
VAST Data's DASE (disaggregated shared-everything) architecture, large-scale unified namespace and GPU Direct support serve many leading AI customers internationally; its software stack (database/data-platform direction) evolves actively and aligns closely with NVIDIA reference architectures (public-source basis). For well-funded, NVIDIA-centric clusters needing EB-scale unified namespaces, VAST is a strong candidate.
Mingxin FX's differentiation
Scenario focus: no all-in-one data platform — we concentrate on the two high-leverage scenarios of inference KV cache tiering and model loading, with measured throughput +29–40% on the 480B production form (R2/R3).
Domestic platforms as first-class citizens: ROCm-level source adaptation and measurements on MI308X/Ascend/MetaX (R1/R5/R9) — coverage that overseas vendors serve thinly.
Cost and compliance: domestic direct procurement, RMB settlement, published fully-populated reference prices (¥2,014/TB class); low friction on domestic-substitution and data-export compliance.
FAQ
Is there a Chinese company benchmarking against VAST?
Positioning differs: Mingxin does not benchmark against their full product line — we go deep and reproducible on the KV cache tiering niche. For full-scope data-platform needs, test several vendors side by side.
How should performance data be compared?
All Mingxin figures carry platform and conditions (480B, MI308X, production form) and are reproducible (R8). Cross-product comparison is only meaningful under the same workload in a joint test — which we support.
Does VAST support non-NVIDIA platforms?
Refer to VAST's official statements. Mingxin has signed measured reports on AMD/Ascend/MetaX (R1–R5, R9) — the reason we can commit to gate-based acceptance.
Data sources (verifiable)
Related reading
- Mingxin FX vs WEKA: The KV-Cache Extension Scenario Compared
- Mingxin FX vs Huawei OceanStor A Series (UCM): How to Choose
- NVMe-oF Inference Storage: Measured Results of a RoCEv2 All-Flash Array
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.