Chris Engelbert 
Running large language model inference at scale has quietly turned into a storage problem. Every active session holds a key-value cache of attention state in expensive GPU high-bandwidth memory, and that cache grows with context length and the number of concurrent users. When it no longer fits, you either recompute it from scratch or drop it, and both choices waste GPU cycles you already paid for. KV cache offloading is the technique that gives those bytes somewhere fast to go, and it is making low-latency block storage a first-class citizen in the AI inference stack.
What KV Cache Offloading Is
During inference, a transformer model computes a key and value tensor for every token in the context. These tensors are cached so the model does not recompute attention over the entire sequence on each new token. That structure is the KV cache, and it lives in GPU HBM, the fastest and most expensive memory in the system.
The cache scales with two things: the length of the context window and the number of concurrent sessions. A handful of long-context chats, agents, or retrieval-augmented prompts can consume more HBM than the model weights themselves. Once HBM fills up, the serving engine has to evict cache entries, and the next time that context is needed the GPU recomputes it from the raw tokens. Recompute is pure overhead: it burns compute that produces no new output.
KV cache offloading addresses this by moving cold or overflow cache out of GPU memory into a tiered hierarchy: CPU RAM first, then fast local NVMe, then networked storage. The hot working set stays in HBM while older or less active state spills downward. Crucially, offloaded cache can be reused. Prefix caching and prompt caching let many requests that share a common system prompt or document context read the same precomputed KV cache instead of recomputing it per request. Reuse from a fast tier is dramatically cheaper than recompute on the GPU, which is why NVIDIA and several inference frameworks have been building KV cache extenders and offload tiers through 2026.
Why It Is a Storage Problem
The moment cache leaves the GPU, you are making a storage decision. Each tier in the memory hierarchy trades latency for capacity and cost, and the engineering goal is to keep the GPU fed without stalling it. CPU RAM is fast but limited and still pricey at scale. Local NVMe gives you orders of magnitude more capacity per dollar at latencies low enough to beat recompute. Networked storage extends that pool across many nodes so cache can be shared and offloaded beyond a single server.
The hard requirement is throughput and latency together. KV cache blocks are read and written constantly, often in large sequential bursts during prefill and smaller reads during decode, so the storage layer has to deliver very high IOPS and sub-millisecond latency. Anything slower than recompute defeats the purpose. That profile points directly at NVMe-class media reached over a low-latency fabric, not at general-purpose file or object storage.
| Tier | Typical latency | Capacity | Relative cost | Role in KV cache offload |
|---|---|---|---|---|
| GPU HBM | Nanoseconds | Smallest | Highest | Hot working set, active attention state |
| CPU RAM | Tens to hundreds of ns | Limited | High | First spill tier for warm cache |
| Local NVMe | Tens to hundreds of µs | Large | Moderate | Overflow and reusable prefix cache |
| Networked NVMe-oF | Sub-millisecond | Largest, pooled | Lowest per GB | Shared, scale-out cache across nodes |
Read the table top to bottom and the trade is clear: you give up a little latency at each step to gain a lot of capacity and a much lower cost per byte. As long as each tier stays faster and cheaper than recomputing on the GPU, offloading wins, and the networked NVMe-over-Fabrics tier is what lets the pool grow beyond a single box.
The Storage Layer for KV Cache Offload
Simplyblock is built for exactly the profile that KV cache offloading demands. It is NVMe-first software-defined block storage, built on SPDK, delivering sub-millisecond latency and high IOPS over NVMe over Fabrics. It speaks both NVMe/TCP and NVMe/RoCE, so you can match the fabric you already run, standard Ethernet for broad reach or RoCE for the lowest latency, without locking into a single transport.
Because it is scale-out, the networked tier grows as your fleet grows, and offloaded cache can be pooled and shared across nodes rather than trapped on one server. It runs on the same bare metal as your GPUs for the shortest possible path, or fully disaggregated so a dedicated storage layer serves many GPU hosts. Intelligent tiering keeps the hottest blocks on the fastest media, and the Kubernetes-native CSI driver fits the way most inference platforms are already deployed. The result is a low-latency NVMe block layer that sits naturally beneath an inference engine’s offload tier.
🚀 Keep inference fast as context windows and concurrency keep climbing. Simplyblock provides the NVMe-first, sub-millisecond block storage layer that suits KV cache offload tiers and demanding AI inference workloads. 👉 Explore simplyblock NVMe over Fabrics storage
This pattern matters most where GPUs run at scale and margins are thin. Specialized GPU providers and AI-native clouds, often called neoclouds, live or die on GPU utilization, and the same low-latency storage principles apply whether you build on a hyperscaler or a sovereign platform. For more on that landscape, see our notes on storage for neoclouds and on European AI cloud providers.
Questions and Answers
What is KV cache offloading? It is the practice of moving a language model’s key-value attention cache out of scarce GPU memory into a tiered hierarchy of CPU RAM, fast local NVMe, and networked storage. The hot working set stays in GPU HBM while colder or overflow cache spills to cheaper, higher-capacity tiers, so the GPU can serve longer contexts and more concurrent sessions without recomputing or dropping cache.
Why not just add more GPU memory? GPU high-bandwidth memory is the most expensive and most constrained resource in an inference server, and you cannot add it independently of the GPU itself. Scaling memory by buying more GPUs is far more costly per byte than adding RAM or NVMe, and much of that capacity would sit idle holding cold cache. Offloading lets you use the expensive HBM for active work and push the rest to tiers that cost a fraction as much.
What storage latency does KV cache offload need? It needs sub-millisecond latency and high throughput, because the offloaded tier only helps if reading cache back is faster than recomputing it on the GPU. KV cache traffic is read and write heavy, with large bursts during prefill, so the layer has to sustain high IOPS as well. That combination points to NVMe-class media accessed over a low-latency fabric such as NVMe over Fabrics.
Does simplyblock have a KV cache product? No, and we will not pretend otherwise. Simplyblock does not ship a bespoke KV cache feature. It is the low-latency, NVMe-first block storage layer that the NVMe and networked tiers of a KV cache offload setup can be built on, providing sub-millisecond latency and high IOPS over NVMe/TCP and NVMe/RoCE so an inference engine’s offload tier has fast, scalable capacity underneath it.
How does this relate to neoclouds and AI inference at scale? Neoclouds and other GPU-dense platforms compete on how much useful work they extract from each GPU. KV cache offloading raises effective utilization by letting a given GPU serve longer contexts and more concurrent sessions, but only if the storage tier keeps up. A scale-out NVMe-over-Fabrics layer that runs near the GPUs or fully disaggregated is what makes that economical across a large fleet.
