Budget for tokens that are still in flight.
Autoregressive transformers can reuse cached attention keys and values instead of recomputing them for every generated token. For full-attention layers, cache storage grows as the sequence grows. Concurrent sequences add more state. Sliding-window layers and hybrid architectures behave differently, so a single context-length multiplier cannot describe every model. Hugging Face documents the distinctions between dynamic, static, offloaded, and quantized caches; the serving engine determines which are available for your checkpoint.
A worked example, not a model recommendation.
Consider a hypothetical full-attention model with 32 layers, 8 KV heads per layer, head dimension 128, and a 16-bit cache. One cached token needs 2 × 32 × 8 × 128 × 2 = 131,072 bytes for keys and values. At 8,192 tokens, that is 1 GiB for one sequence; eight independent sequences would need 8 GiB. This simplified calculation excludes allocator overhead and assumes identical layers, no shared prefix, no sliding window, and no cache compression. It is not a measurement of a model in our directory.
Separate the advertised limit from your service limit.
A model's supported context is a capability ceiling, not a claim that every GPU can serve that many tokens. Decide how much space your application needs for input and generated output together. For a document assistant, start with the actual distribution of retrieved text and conversation history. Our suggested experiment is to test short, typical, and long requests separately, then repeat at several concurrency levels while recording peak memory and failures.
Diagnose the point of failure.
Record whether the failure occurs during loading, prompt processing, or generation. Reduce one variable at a time: maximum sequence length, concurrent requests, or the size of image and audio inputs. Keep a successful configuration as a baseline. Cache offloading and cache quantization can change the trade-off, but require compatible implementations and fresh latency and quality checks. Do not convert one successful short request into a production capacity claim.
Put the context to work.
Explore the models, compare the fundamentals, and start with a clearer picture.
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