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Model parameters stay fixed, while attention state grows with tokens, layers, precision, batch size, and concurrent home AI requests.

Continuous batching keeps the accelerator busy, but fairness depends on how service is measured and divided across unequal household requests.

A sliding window bounds active KV memory by retaining only a recent token range, trading full-history attention for predictable inference capacity.

Prefill uses parallel matrix work across many input tokens, while decode advances one accepted token at a time and repeatedly reads model state.

Speculative decoding reduces serial target-model steps by drafting several future tokens and verifying them together without changing exact decoding results.

Quantization introduces numerical approximation; quality depends on bit width, method, calibration data, model scale, and the workflow being tested.

KV cache grows as a model retains attention keys and values for more prompt and output tokens across every transformer layer and active request.

Accelerator scheduling decides which user enters the model, shares each iteration, keeps cache state, or waits behind longer and higher-priority work.