Hugging Face publishes inference guide on prefill, decode and KV cache; more guides planned
Overview
Hugging Face has released the first in a planned series of conceptual guides on LLM inference, covering prefill, decode and the KV cache, according to Hugging Face's Merve Noyan on X. Noyan says the guide explains what to optimize for when trying to get the most performance out of a local setup.
Noyan also says guides on speculative decoding and then quantization are in progress, and points readers to the Llama App docs page comparing prefill and decode.
Written by AI from the articles below · updated Oct 9, 9:37 AM ET
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Developments
2 developments
- Oct 9, 9:06 AM ET · 1 articleHugging Face working on speculative decoding and quantization guidesMerve Noyan: Hugging Face working on speculative decoding and quantization guides
- Oct 9, 9:05 AM ET · 1 articleConceptual guide on prefill, decode, and KV cache for local inference optimizationMerve Noyan: Hugging Face publishes a guide on LLM inference prefill, decode, and KV cache
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merve@mervenoyannHugging Face working on speculative decoding and quantization guidesAIMerve Noyan, of Hugging Face, says a new guide on speculative decoding is in progress, followed by one on quantization. She points readers to the Llama App docs page comparing prefill and decode.
merve@mervenoyannHugging Face publishes a guide on LLM inference prefill, decode, and KV cacheAIHugging Face has released the first in a series of conceptual guides on inference, covering prefill, decode, and the KV cache. The post says the guide explains what to optimize for when trying to get the most performance out of a local setup.

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