AI inference glossary
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NVMe offloading

Also known as NVMe offload, SSD offloading, flash KV cache offload

In plain English

NVMe offloading extends the KV cache one tier further, onto local SSDs, so prefixes that no longer fit in GPU or CPU memory can still be reloaded later.

Technical definition

NVMe offloading stores reusable KV cache blocks on NVMe SSDs beneath the HBM and host DRAM tiers, trading slower reloads for a much larger retrievable KV working set.

Engineering details

Each step down the memory hierarchy multiplies capacity and divides bandwidth, so the SSD tier only pays off when reloading a long prefix still beats recomputing it. KV cache managers such as LMCache and Mooncake Store already support local NVMe backends alongside DRAM and remote storage. The tier helps most when the reuse working set exceeds any plausible DRAM budget or when sessions return after idle gaps long enough that DRAM eviction has already discarded them.

Why it matters

NVMe offloading effectively lengthens cache lifetime for long-lived agent sessions, which matters as agents wait on tools, humans, or CI for minutes at a time. It is workload dependent: a high-concurrency deployment where DRAM offloading already degrades latency will not be rescued by an even slower tier, because the bottleneck is reload bandwidth rather than capacity.

How to read it in InferenceX

AgentX v1 measures HBM and DRAM tiers and defers NVMe offloading, with SSD/NVMe KV offloading planned as a fast follow to grow the working set beyond DRAM. The current 5 minute idle cap on replayed streams may rise alongside it so that longer cache lifetimes become measurable.