• context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
• attention — The mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.
• token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.

Concrete numbers for what block-level sparse attention buys at million-token contexts, plus the serving paths — SGLang, vLLM, TensorRT-LLM, NeMo — for running the model from workstation to rack scale.

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