Comparisons
The selection-comparison library. Principles: rivals' strengths stated honestly, only public-source information quoted, and every Mingxin figure carries a report ID — the goal is helping you choose the right solution, including cases where that is a competitor's.
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.
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.
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.
Mingxin FX vs DDN: Route Differences from HPC Storage to Inference Acceleration
DDN is the incumbent power in HPC/AI training storage, with deep EXAScaler share in supercomputing. Mingxin FX does not do general HPC storage — it focuses on inference KV tiering and load acceleration, winning on reproducible measurements and domestic-platform adaptation.
Mingxin FX and NVIDIA Dynamo: Complementary, Not Competing
Dynamo is NVIDIA's open-source distributed inference framework managing KV routing and tier scheduling; it needs an external storage tier to absorb offloaded KV. Mingxin FX is that tier — and on non-NVIDIA platforms Mingxin provides a complete alternative stack.
External KV Array vs Local NVMe vs NFS: A Three-Way Measured Comparison
Same-workload measurements: FX100 beats a local NVMe drive by 26–32% on TTFT and 29–40% on throughput; beats no-external-storage re-compute by 8.6–20×; beats NFS model loading by 6.2–9.3×. All three baselines are report-verifiable.
Storage Acceleration vs Adding GPUs: Two Ways to Spend the Same Budget
Adding GPUs yields linear, expensive marginal capacity. When the bottleneck is KV re-compute, loading, or switching idle time, storage acceleration buys a 29–40% throughput gain for about a tenth of GPU spend (measured). Locate the bottleneck first, then decide.
Mingxin FX and Mooncake: Open-Source KV Transport Layer Meets Storage Hardware
Mooncake is an open-source KVCache-centric disaggregated inference architecture (transfer engine + store interface); it defines how KV moves but still needs high-performance storage hardware to land on. Mingxin FX is the domestic option for that layer.
Mingxin FX vs JuiceFS: Division of Labor Between a Block Array and a Cloud-Native Filesystem
JuiceFS is a cloud-native filesystem on top of object storage, strong in elastic capacity and multi-cloud data management. FX is a high-performance NVMe-oF block tier, strong in latency-sensitive KV read-back and weight loading. They are usually combined rather than either-or.
External All-Flash vs Memory-Only Tiering: Is a RAM-Only Second Tier Enough
Host memory is the essential second tier (best latency) but typically holds 1–2 TB and cannot be shared across nodes. The external all-flash third tier multiplies capacity by two orders of magnitude (184.3 TB per array) and adds cross-instance sharing, measured to support +29–40% throughput.