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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteContext length is how many tokens a model and runtime can process in a sequence. The KV cache holds attention keys and values for earlier tokens so the model can reuse them during generation. A model’s advertised context limit is not a promise that your computer can fit that many tokens: usable context also depends on cache memory, model architecture, runtime settings, and how many sequences run at once.
What is context length in a local LLM?
Context length is the token capacity of a model’s input-and-generation sequence. Tokens are pieces of text, not necessarily whole words. Depending on the model and runtime, the sequence may include a prompt, conversation history, and tokens generated in response.
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Three limits matter in practice: what the model supports, what the runtime allows you to request, and what the available memory can accommodate. The limits are related but not interchangeable. A setting can accept a large maximum sequence length even when the cache pool cannot schedule a request of that size.
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What does the KV cache store?
During autoregressive generation, a model produces tokens one at a time. At each step, attention layers calculate key and value states for tokens. The KV cache retains those states for earlier tokens, so the model can reuse them instead of recalculating them at every step.
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Cache tensors are stored across model layers, and their dimensions include sequence length, attention heads, and head dimension. In ordinary full-attention layers, adding tokens therefore adds cached state. Hugging Face’s cache documentation describes the cache strategies and their behavior.
Why does a longer context use more memory?
For a conventional dynamic full-attention cache, every additional token adds key and value data across the cached layers. More tokens, more concurrent sequences, or a larger cache data type can increase the required memory. The model’s number of layers, key/value heads, and head dimension also matter.
Not every architecture grows in the same way. With sliding-window attention, a layer’s cache can stop growing once it reaches its window. Chunked or hybrid attention can also change cache behavior. That does not mean a model has unlimited effective context: cache-allocation behavior alone does not establish that information outside a layer’s window remains directly available to every layer.
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How can I estimate KV-cache memory?
For a simplified dense full-attention cache, estimate the storage for keys and values with this relationship:
Cached layers × 2 × tokens per sequence × KV heads × head dimension × bytes per value × concurrent sequences
The factor of two accounts for keys and values. Use the model configuration and intended workload to fill in the terms; the result is a starting estimate, not a guarantee of runtime allocation.
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Actual use can differ because of grouped-query attention, sliding-window or hybrid layers, quantization metadata, padding, paging, and runtime-specific allocation pools. Leave room for model weights and other runtime memory as well. The official documentation describes the relevant tensor dimensions and cache behavior, but does not establish a universal GB-per-token figure for local LLMs.
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A model’s supported maximum, the runtime’s configured maximum, and the cache pool’s capacity are separate constraints. The pool must have enough capacity for the requested sequences, in addition to space for weights and runtime overhead.
For example, vLLM’s v0.31.0 engine arguments documentation provides controls for cache memory budgets. The vLLM.cpp server reference documents a token pool configured by block count and block size; a request longer than the pool cannot be scheduled. Exact flags and behavior depend on the backend version, so check the documentation for the runtime you use.
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Sliding-window layers add another distinction: a configured maximum sequence length can exceed the point at which those layers’ caches grow. That is a cache behavior, not proof that every layer attends directly to every token in the full sequence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which cache strategy should I use?
Cache strategies differ in when they allocate memory and where they keep it. Hugging Face identifies DynamicCache as the default cache class for all models in its documentation; a runtime or model may expose only a subset of the options below.
| Strategy | How it behaves | Main trade-off |
|---|---|---|
| Dynamic | Grows as tokens are processed. | Allocation follows actual use, but memory use grows with the sequence. |
| Static | Preallocates cache capacity up to a set limit. | Can help compilation, but may reserve space or perform work that shorter requests do not use. |
| Offloaded | Moves most layer cache state to CPU memory to reduce GPU memory use. | Transfers state between CPU and GPU, which can reduce throughput. |
The appropriate choice depends on your sequence-length patterns, memory pressure, and latency needs. See Hugging Face’s cache strategy documentation for the strategies it supports.
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Should I quantize or offload the KV cache?
Quantization stores cache values in a lower-precision format to reduce memory use. It can make more cache capacity available, but it is not automatically faster or quality-neutral. Hugging Face warns that cache quantization can harm latency for short contexts when GPU memory is already sufficient. vLLM documents FP8 cache options, including the ability to leave selected layer types in their native dtype. These sources do not establish a universal speedup or quality penalty.
Offloading can relieve GPU memory pressure by keeping most cache state in CPU memory, but moving that state can reduce throughput. Whether either approach helps depends on the model, workload, and runtime implementation.
Before changing cache precision or location, identify whether cache capacity is actually the bottleneck. Compare the same model and prompts under the settings you expect to use; do not assume memory savings will preserve speed or output behavior.
How should I plan for multiple conversations?
Cache capacity is a shared resource when a runtime serves multiple sequences. A cache pool that can hold one long sequence may not have room for several long sequences at once. More cache memory can support longer sequences, more concurrent work, or a mix of both; the runtime’s scheduling policy determines how it uses the pool.
vLLM’s cache configuration documentation describes per-GPU cache memory sizing, and its optimization guidance explains that insufficient cache space under long-context loads can lead to request preemption. Check the limits and behavior of your specific engine rather than planning around a single conversation in isolation.
What should I check if I run out of GPU memory?
- Reduce the requested context to the amount your task needs, rather than setting the model’s largest advertised limit by default.
- Reduce simultaneous sequences if the serving engine allocates cache across concurrent requests.
- Check cache settings for the runtime’s pool size and whether cache quantization or CPU offloading is supported.
- Recheck the workload after each change, including latency and throughput, not just whether the request starts.
- Consider more memory or multiple devices only if cache capacity remains the measured bottleneck and your engine supports distributing the model or cache.
Each option has trade-offs: quantization can affect latency or numerical behavior, and offloading can reduce throughput. A larger GPU is one possible remedy for a genuine cache-capacity limit, not a prerequisite for every local LLM setup.
Quick Recap
What should I compare between two local LLM setups?
- Model-supported and runtime-configured context limits.
- Cached layers, KV-head count, head dimension, and the resulting cache storage per token.
- Cache dtype and whether the backend supports quantization.
- Cache behavior: dynamic, static, paged, sliding-window, or hybrid.
- Total cache-pool capacity, alongside memory reserved for weights and runtime overhead.
- Maximum concurrent sequences and how the runtime schedules them.
- Latency and throughput on the prompts and concurrency you actually expect to use.
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