Everyone talks about making LLM inference faster. A surprising amount of serving…
This is a dev post classified by Jev as Hosting & infra (a tutorial), kept by the Dev Radar because it carries real work, not commentary.
Everyone talks about making LLM inference faster. A surprising amount of serving performance, however, comes from work happening around the model rather than changes to the model itself. The server can avoid recomputing old work, keep batches occupied, use KV-cache memory more efficiently, or schedule prefill and decode differently. These 6 techniques each attack a different source of waste. 1/ Prefix caching Suppose many requests begin with the same system prompt, document or few-shot examples. The KV states for that shared token prefix have already been computed once. Prefix caching lets
Posted by Tech with Mak (45k followers) 1 h ago · 18 likes · 654 views · view the original post on X. Kept by the Dev Radar as Hosting & infra.
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