Mingxin FX vs WEKA: The KV-Cache Extension Scenario Compared
WEKA's parallel filesystem and Augmented Memory Grid lead the KV-extension direction in the NVIDIA ecosystem. Mingxin FX achieves the same class of capability with an NVMe-oF block layer + the open LMCache stack, strong on domestic platforms and reproducible verification.
Stated honestly: WEKA's strengths
WEKA's high-performance parallel filesystem has years of standing in HPC/AI training; its inference-facing Augmented Memory Grid (KV-cache HBM extension) is the same idea class as Mingxin's KV tiering, aligns tightly with the NVIDIA ecosystem (GDS, Dynamo/NIXL) and has rich international references (public-source basis). This also corroborates that external KV tiering is an industry-recognized correct direction.
Mingxin FX's differentiation
Technical route: open software stack (vLLM/LMCache) + standard NVMe-oF hardware — no proprietary client or protocol lock-in; customers can audit every layer.
Domestic compute coverage and measurements: 480B production-form measurements on MI308X (R2/R3), Ascend/MetaX measurements (R9/R5), and a parallel-read patch contributed to the open-source community (R8).
Verification style: signed reports + code export + gated joint tests, all numbers reproducible — we do not ask customers to believe, only to re-measure.
FAQ
Parallel filesystem or NVMe-oF block layer — which is better?
Each has its domain: filesystems favor unified namespaces and training data; KV tiering workloads are chunked object reads/writes where block + LMCache suffices with a thinner stack. Choose by workload; no need to take sides.
Can the two coexist?
Yes: training data on a parallel filesystem and KV tiering on FX arrays is a sensible combined architecture with no conflict.
Does Mingxin have international references?
Honestly: Mingxin's public measurements are mainly on domestic platforms (R1–R9) and international delivery is in its early stage — exactly why we made all data open and reproducible, letting evidence speak instead of references.
Data sources (verifiable)
Related reading
- Mingxin FX vs VAST Data: Two Routes in AI Storage
- Mingxin FX vs DDN: Route Differences from HPC Storage to Inference Acceleration
- Productionizing LMCache: From Open-Source Component to Signed Benchmarks
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.