A KV cache stores the attention keys and values an LLM has already calculated, so it can generate the next token without recalculating those representations for the entire conversation. That saves repeated work, but the cache grows with the sequence and with the number of requests being served at once—making it a significant consumer of accelerator memory.
What a KV cache does during LLM inference
When an LLM processes a prompt, it first runs a prefill stage: the model processes the input tokens and calculates attention state for them. It then generates output autoregressively, one token at a time. On each decoding step, the new token’s query attends to keys and values from earlier tokens.
Without a cache, the model would need to recalculate earlier tokens’ key and value representations at every step. With a KV cache, it retains those numerical tensors and appends the new token’s entries as generation continues. Hugging Face’s current inference-optimization documentation and the 2023 PagedAttention paper describe this reuse as a way to avoid repeated calculation.
Think of the cache as a growing record of attention-ready state—not a summary or a natural-language memory. It contains numerical representations rather than directly readable text, and it does not let a model exceed its context limit.
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Why KV cache consumes so much GPU memory
The cache grows as tokens are added. Its total demand also rises with the number of active sequences, because a serving system must retain state for each request that is still being processed. The precise footprint depends on the model architecture and runtime configuration; there is no single reliable per-token figure for every LLM.
Cache is only one of the things that must fit in accelerator memory: model weights and other runtime state need space too. When cache demand is high, a serving system may have room for fewer concurrent requests or a smaller batch, which can constrain throughput. Longer contexts and higher concurrency can therefore increase memory pressure together.
Prefill and decoding use the cache differently. Prefill creates entries for the input; decoding repeatedly uses the growing cache while producing output. Which phase or resource limits performance depends on the workload, model, and hardware, so KV cache is an important constraint—not a universal explanation for every slow inference request.
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How inference systems manage the cache
Cache strategies trade off allocation flexibility, reserved memory, transfer costs, and implementation support. The table compares their basic behavior; no method is best for every workload.
| Approach | How it manages cache | Main trade-off |
|---|---|---|
| Dynamic cache | Grows as generation proceeds. | Flexible sizing, but changing allocation shapes can complicate graph compilation and memory management. |
| Static cache | Reserves a configured maximum size in advance. | Can work with graph compilation, but may reserve more memory than a request ultimately uses. |
| Paged or block cache | Allocates fixed-token blocks on demand; blocks need not occupy contiguous memory. | Helps manage fragmentation and can enable block reuse in supported serving patterns; requires compatible runtime support. |
| Prefix caching | Reuses cached blocks when request prefixes match under the serving engine’s cache identity rules. | Useful for matching prefixes, but does not make semantically similar or differently represented prompts interchangeable. |
| CPU offloading | Keeps some cache data in host memory and transfers it as needed. | Can relieve GPU-memory capacity pressure, while adding host-device data movement that affects performance. |
| Lower-precision cache or compression | Uses a supported representation that requires less storage. | Availability and effects depend on model, backend, and configuration; lossy techniques can affect retained information or output fidelity. |
Dynamic and static allocation
A dynamic cache allocates as a request grows, avoiding a commitment to the maximum length up front. A static cache instead reserves a chosen maximum, giving the runtime a stable shape that can be more suitable for compilation. Hugging Face says pairing its static cache with torch.compile can deliver “up to a 4x speed up”; this is a maximum reported in its rolling documentation accessed in 2026, and the documentation cautions that actual results vary with model size and hardware.
PagedAttention and prefix reuse
PagedAttention organizes KV state into fixed-token blocks, which can be placed non-contiguously and allocated when needed. This avoids requiring each request’s cache to occupy one growing contiguous region. vLLM’s documentation describes prefix reuse by block identity and preceding prefix tokens: when the relevant prefix matches under the engine’s rules, a request can reuse those blocks. This is a cache identity match, not a way to share state between arbitrary prompts that merely mean the same thing.
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CPU offloading and compression
Offloading can make more cache capacity available than GPU memory alone provides, but it shifts part of the cost to data movement between host and accelerator. A vLLM post from January 2026 discusses the transfer mechanics and throughput implications. Whether offloading helps depends on how much data must move and how that cost interacts with the workload.
Lower-precision cache formats and other compression approaches can reduce storage needs when the model and backend support them. vLLM’s versioned CLI documentation exposes KV-cache data-type options, but support is configuration-specific. Check the documentation for the exact engine version, model, and backend before relying on a particular format or assuming its quality and performance effects.
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Published numbers illustrate results in particular configurations; they are not directly interchangeable benchmarks. Hugging Face’s “up to a 4x speed up” figure refers to static KV cache paired with torch.compile, with results varying by model size and hardware. The figures below belong to vLLM’s 2023 project post and the PagedAttention paper’s evaluated comparisons, respectively:
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- vLLM reported “up to 24x higher throughput” compared with Hugging Face Transformers. This is the project’s reported result, not a universal or independent comparison across current systems.
- In discussing the systems covered by its 2023 project post, vLLM said fragmentation and over-reservation wasted “60% – 80% of memory.” That diagnosis describes the systems in that post, not every modern inference engine.
- The 2023 PagedAttention paper reported a “2-4×” throughput improvement under its evaluated comparisons to then state-of-the-art systems at the same latency. The result is tied to those comparisons and evaluation conditions.
These reports do not establish a current, controlled, apples-to-apples winner across cache strategies, engines, models, and workloads.
How to choose a cache strategy for a workload
Start with the constraint you need to address, then verify that the model and serving stack support the proposed technique. Compare options using the workload you actually expect to serve:
- Memory capacity: account for model weights and runtime state as well as cache, and consider how context length and concurrent requests change demand.
- Allocation behavior: check whether requests have varied lengths and whether reserved space or fragmentation is limiting useful capacity.
- Transfer costs: for CPU offloading, measure the effect of host-device traffic rather than treating added capacity as free.
- Concurrency and throughput: evaluate the target mix of prompt lengths, output lengths, and active requests; results from another workload may not transfer.
- Model and backend support: confirm that the selected engine version, model, hardware, and cache format work together.
- Retention and fidelity: determine whether compression or eviction changes what context remains available or affects output quality.
- Operational complexity: weigh any memory or throughput benefit against the additional configuration and support burden.
The useful outcome is not simply the smallest cache or the highest isolated benchmark number. It is a cache policy that fits the target workload’s memory, latency, concurrency, and retention requirements.
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