AI platform architects, ML infrastructure leads, and inference platform owners evaluating how to cut GPU recompute costs and raise effective utilization on clusters running long-context, high-concurrency, or multi-node inference workloads.
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When GPU memory runs out, evicted context triggers full prefill recomputation. In production deployments, more than half of all GPU cycles go to rebuilding context the cluster already paid for, and utilization dashboards can't tell recompute apart from real token generation.
MemKV fills the G3.5 tier the memory hierarchy was missing: a shared, cluster-wide KV cache pool between local GPU/CPU memory and durable object storage, reached over RDMA at microsecond speed and petabyte scale, on standard NVMe flash.
Recovering those cycles holds effective GPU utilization near 90%, cuts cost per token 40 to 60%, and puts a petabyte of context memory at under $80,000, all without touching GPU compute or rewriting the application.
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