LLM Inference Unmasked: Prefill's Parallel Power and KV Cache's Clever Hack
Everyone figured LLMs recompute your whole prompt for every word. Wrong. Prefill and KV cache flip that script, slashing compute while scaling to novels.
β‘ Key Takeaways
- Prefill processes full prompts in parallel via causal attention, GPU heaven.
- Decode generates serially but caches KV to skip recomputes.
- KV cache scales linearly in memoryβnext frontier for million-token contexts.
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Originally reported by Machine Learning Mastery
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