MinIO has developed a petabyte-scale MemKV caching system tailored for Nvidia GPUs, deployed on top of its AIStor object storage platform.
GPU clusters running inference require high-bandwidth memory (HBM) to store context, vectorized tokens and intermediate key-value (KV) pairs. Once GPU HBM is saturated, data cascades down to CPU DRAM and NVMe SSDs, managed by Nvidia BlueField-4 (BF4) DPUs. When these tiers reach capacity, MinIO AIStor acts as the final storage backup. Nvidia’s STX architecture governs this multi-layer cache hierarchy, and MemKV complies with the standard to deliver persistent, shared context across GPU clusters at superior scale.
AB Periasamy, MinIO co-founder and co-CEO, commented: “The industry has been papering over context loss for years because, at small scale, you may absorb the recompute tax. At today’s high GPU density for hyperscalers and neoclouds, this is no longer viable.
Recomputing generated context wastes power; for clusters with thousands of GPUs, it creates fundamental structural inefficiency. Large-scale inference requires purpose-built infrastructure, and MemKV is designed specifically for this data path.”
For the first time, MinIO enables shared context pools for entire GPU clusters at microsecond-level latency matching inference workflows, avoiding millisecond delays from conventional external storage. Without sufficient cache tiers, GPUs waste resources on repeated context recalculation.
In a 128-GPU deployment with 128K-token context length, MemKV improved time-to-first-token under production loads and boosted GPU utilization from 50% to over 90%, generating an estimated $2 million annual compute cost saving.
Purpose-built for Nvidia STX architecture, MemKV supports Nvidia Dynamo and NIXL caching tools. It delivers petabytes of shared context memory at SSD-level costs, decoupling cache scaling from GPU compute resources. Its core features are listed below:
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Native BF4 STX support: Runs as an ARM64 binary within STX infrastructure, embedded in storage rather than separate x86 storage servers.
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End-to-end RDMA transport: Transfers KV cache between GPU memory and NVMe via RDMA, bypassing conventional file and object storage protocols.
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GPU-optimized block size: Uses 2–16 MB blocks for GPU throughput demands, instead of legacy 4 KB storage blocks.
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Wire-speed performance: Optimized for Nvidia Spectrum-X Ethernet and PCIe Gen6 to maximize physical fabric throughput.
MemKV directly transfers data from NVMe SSDs to AI pipelines over RDMA, eliminating HTTP overhead, file system translation and intermediate storage servers.
MinIO categorizes rival context memory solutions into two types: non-sharable local NVMe (G3) and general-purpose shared storage (G4). It positions MemKV as a purpose-built G3.5 tier, distinguishing itself from generic storage products.
The firm emphasizes that legacy vendors’ G3.5 offerings still retain redundant protocol nodes, metadata services and file translation layers. These layers ensure durability and consistency for training data and model weights, yet they are unnecessary for ephemeral, recomputable KV cache optimized for 2–16 MB data blocks.
Hardware RAID vendor GRAID and storage firm WEKA also provide STX-compatible KV cache solutions. A broad range of storage vendors support Nvidia STX, including Cloudian, Dell, DDN, Everpure, Hammerspace, Hitachi Vantara, HPE, Lightbits/ScaleFlux, NetApp, Nutanix, Peak:AIO, Pliops and VAST Data.
Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
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Email: yangyd@qianxingdata.com
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