Purpose-Built Context Memory Store for AI Inference

MinIO MemKV delivers transformative improvements to both TTFT (Time to First Token) and TPOT (Time Per Output Token) in AI inference workloads by providing petascale, native flash-based context memory accessed end-to-end over 800 GbE RDMA.

Built for the G3.5 Layer

Designed exclusively for AI inference and built from the ground up for the G3.5 layer of the GPU memory hierarchy.

Native to STX Infrastructure

Runs directly within NVIDIA STX® as a single ARM64-native binary embedded in the storage tier, not deployed on separate x86 servers connected over the network.

End-to-End RDMA Transport

KV cache moves from GPU memory to NVMe over RDMA, bypassing file system and object protocols entirely. No CPU in the data path, no protocol translation overhead.

GPU-Native Block Sizes

Operates in 2–16 MB blocks optimized for throughput-oriented GPU access patterns, not the 4 KB blocks designed for legacy storage workloads.

Petascale KV Cache Capacity

Deploy petabytes of G3.5 KV cache across the inference cluster, virtually eliminating redundant prefill computations and significantly improving GPU efficiency.

Wire-Speed Fabric Performance

Built for NVIDIA Spectrum-X 800 GbE networking and PCIe Gen6, driving throughput to near wire speed across the physical fabric.

Elastic Independent Scaling

Scale GPU compute and shared context memory independently. Add KV cache capacity without provisioning additional GPU nodes, and vice versa.

Why MemKV is Different

Conventional inference architectures store KV cache in per-GPU HBM, which is a scarce, expensive resource that forces a hard tradeoff: keep context in memory and starve the model, or evict it and pay the recompute penalty on every request. Neither path is acceptable at scale. MemKV eliminates the tradeoff by placing a petascale, flash-backed KV pool at the correct layer of the memory hierarchy, accessed over RDMA without touching a file system or object protocol.

G3.5 Native, Not Retrofitted

Lives at the correct layer between GPU HBM and object storage, not an appliance bolted onto existing infrastructure after the fact.

RDMA From GPU to NVMe

Data moves from GPU memory to flash over RDMA with no file system, no object protocol, and no CPU in the critical path.

Shared Pool Across the Cluster

Every inference node draws from the same petascale KV store, eliminating per-GPU recomputation as a recurring cost.

Single Binary in the Storage Tier

Runs as one ARM64-native binary embedded in NVIDIA STX, not a separate server cluster connected over the network.

Business Impact

95%+ Sustained GPU Utilization

GPUs stop wasting cycles on context recomputation and run token generation at full throughput. Utilization above 95% is sustained across the cluster, not a single-node peak.

40-60% Lower Cost Per Token

Eliminating over-provisioned GPU memory and recompute cycles cuts production cost per token by 40–60% in inference clusters. GPU compute and context memory scale independently.

White downward arrow icon on black background.

Reduced Power and Operational Overhead

Flash NVMe and RDMA consume a fraction of the energy required by DRAM-scale systems or recomputation-heavy clusters. Cooling and data-center footprint shrink accordingly; power savings alone can cut OpEx by tens of percent at scale.

100x Agentic Scale for Enterprises

Multi-step agentic tasks that previously required prohibitive GPU memory now run at 100x scale with consistent response times. Long-context workloads become economically viable in production.

Shared Memory Tier for Model Providers

A single petascale pool replaces per-GPU cache fragmentation, combining microsecond responsiveness with petabyte-scale capacity across the entire serving infrastructure.