KV cache offloading is the practice of moving a large language model’s key-value (KV) attention cache out of scarce GPU high-bandwidth memory (HBM) and into a tiered memory hierarchy that spans CPU RAM, fast local NVMe, and networked NVMe-over-Fabrics storage. The KV cache stores the attention state for every token already processed in a request, and it grows linearly with context length and the number of concurrent sessions. On modern long-context, high-concurrency inference workloads, that cache outgrows the tens of gigabytes of HBM a GPU has available.
When the cache cannot fit in HBM, an inference server has only bad options: drop cache and recompute prefixes (wasting GPU cycles), evict sessions (hurting concurrency), or refuse longer context windows. KV cache offloading adds a fourth path. It spills cold or overflow cache down a hierarchy of progressively cheaper, higher-capacity tiers and pages it back when needed, while also enabling prefix and prompt cache reuse across requests. This pattern is emerging quickly in 2026, with NVIDIA and partners building KV-cache offload and extender tiers into inference stacks.
How KV Cache Offloading Works
During autoregressive generation, a transformer caches the key and value tensors for every previously seen token so it does not recompute attention from scratch on each step. This KV cache is what makes token generation fast, and it is also what consumes memory. Cache size scales with model dimensions, context length, batch size, and the number of active sessions, so a fleet serving long prompts to many users can need far more cache than the GPU’s HBM holds.
Offloading introduces a memory hierarchy. The hottest, most recently used cache stays in HBM. As HBM pressure rises, the runtime evicts colder blocks downward: first to CPU RAM over the PCIe or NVLink path, then to fast local NVMe, then to networked NVMe-over-Fabrics storage for capacity that no single node could hold. When a request needs an evicted block again, the runtime pages it back up the hierarchy rather than recomputing it.
The other half of the value is prefix and prompt cache reuse. Many requests share a common prefix, such as a long system prompt, a retrieved document, or a shared conversation history. Instead of recomputing that prefix for every request, the offloaded KV cache can be retained and reused across sessions, turning expensive recomputation into a cheap cache lookup. The economics of reuse depend entirely on how fast the underlying tier can serve those cached blocks back to the GPU.
Why KV Cache Offloading Matters for Inference Cost
GPU HBM is the most expensive and most scarce resource in an inference deployment. A high-end accelerator ships with a fixed, relatively small pool of HBM, and that capacity is shared between model weights and the KV cache. When the cache competes with weights for the same memory, operators are forced to cap context length, reduce batch size, or buy more GPUs purely to hold cache they could otherwise store more cheaply.
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Recomputation is the hidden tax. Every time a server drops cache and rebuilds a prefix, it burns GPU compute that could have served new tokens. Offloading converts that wasted compute into comparatively cheap capacity on RAM and NVMe, raising effective GPU utilization. The result is higher throughput per GPU, support for longer context windows without new hardware, and more concurrent sessions on the same fleet, which is the difference between a profitable and an unprofitable inference service.
KV Cache Offloading and Storage Performance
Offloading only pays off if the lower tiers are fast enough that paging a block back is cheaper than recomputing it. That sets a hard performance bar on the storage layer. KV cache blocks must be served back at sub-millisecond latency and at high, sustained throughput, because the GPU stalls while it waits for cache it cannot find in HBM. Slow storage turns offloading from an optimization into a bottleneck.
This is why the networked tier of a KV cache hierarchy is built on NVMe-class media rather than general-purpose storage. NVMe-over-Fabrics extends local NVMe performance across the network, letting GPU nodes reach a shared, disaggregated pool of fast capacity without the latency penalty of legacy network storage protocols. simplyblock supports both NVMe/TCP, which runs on standard Ethernet, and NVMe/RoCE for RDMA-accelerated paths, so the fabric can match the latency budget of the inference stack. The goal is to make the NVMe tier feel like a natural, low-latency extension of the GPU’s memory hierarchy rather than a distant disk.
How Simplyblock Fits KV Cache Offloading
Simplyblock is an NVMe-first, software-defined block storage layer that delivers the sub-millisecond latency and high throughput an offload tier requires. It is not a bespoke KV cache product, and it does not replace the inference runtime that manages eviction and reuse. Instead, it provides the fast, scalable NVMe and networked-NVMe storage on which those NVMe and networked tiers can be built.
Because simplyblock pools NVMe devices into a scale-out cluster exposed over NVMe-over-Fabrics, it can serve KV cache capacity either close to the GPUs on local NVMe or in a disaggregated pool that many GPU nodes share. It speaks both NVMe/TCP and NVMe/RoCE, so platform teams can match the transport to their fabric and latency budget. As context windows and session counts grow, the storage tier scales out independently of the GPU fleet, so operators add cheap NVMe capacity instead of buying GPUs to hold cache. For more on the underlying layer, see simplyblock block storage and the deeper discussion in storage for KV cache offloading.
Related Terms
KV cache offloading sits within a broader set of NVMe and AI infrastructure concepts relevant to platform and inference teams.
Questions and Answers
What is KV cache offloading in LLM inference?
KV cache offloading is the practice of moving a model’s key-value attention cache out of scarce GPU high-bandwidth memory into a tiered hierarchy of CPU RAM, local NVMe, and networked NVMe-over-Fabrics storage. The runtime keeps hot cache in HBM and spills colder or overflow cache to the lower tiers, paging it back when needed. This lets a single GPU serve longer context windows and more concurrent sessions without recomputing or dropping cache.
Why does the KV cache outgrow GPU memory?
The KV cache stores the attention key and value tensors for every token already processed, so its size grows linearly with context length, batch size, and the number of concurrent sessions. A GPU has only tens of gigabytes of HBM, shared between model weights and cache. On long-context, high-concurrency workloads the cache demand quickly exceeds what HBM can hold, which forces eviction, recomputation, or capped context unless the cache is offloaded.
What storage performance does KV cache offloading require?
The lower tiers must return cache blocks faster than the GPU could recompute them, which in practice means sub-millisecond latency and high, sustained throughput on NVMe-class media. The networked tier is typically built on NVMe-over-Fabrics so GPU nodes can reach a shared pool of fast capacity without the latency penalty of legacy network storage. simplyblock supports both NVMe/TCP and NVMe/RoCE so the fabric can match the inference stack’s latency budget.
How is prefix cache reuse related to KV cache offloading?
Prefix or prompt cache reuse means retaining the KV cache for a shared prefix, such as a long system prompt or a retrieved document, and reusing it across requests instead of recomputing it. Offloading provides the capacity to keep these prefixes resident in RAM or NVMe rather than discarding them when HBM fills. The benefit depends on how fast the storage tier can serve cached blocks back, which is why the offload tiers are built on sub-millisecond NVMe-class storage.