LLM Inference Explained: Why the First Token Lags and the Rest Stream Smoothly
Roger_M_Taylor · x · 2026-08-13
This thread clearly explains the two core phases of Large Language Model (LLM) inference and their distinct performance bottlenecks.
- Prefill phase: The model processes all input tokens in a single parallel pass. It is highly compute-intensive, maximizing GPU utilization. This phase is compute-bound and determines the Time-To-First-Token (TTFT).
- Decode phase: The model generates subsequent tokens one by one. Each step requires minimal computation, making it memory-bound.
This fundamental mechanism explains why there is always a brief pause before an LLM outputs its first word, followed by a smooth stream of text.
Related event: Explaining LLM Inference: How Prefill and Decode Stages Affect Speed(2 posts)→
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