LLM Inference Explained: Prefill vs Decode and Why Latency Matters

In this video, we break down the two fundamental stages of LLM inference: prefill and decode. These stages determine how fast a model responds, how latency scales with prompt and output length, and which part of inference dominates in real-world applications. You’ll learn: What the prefill stage is and why it processes the entire prompt at once How the decode stage works and why it is usually faster per token Why long prompts significantly increase time to first token How output length affects decode time linearly Whether prompt length impacts decode speed (and by how much) How KV cache enables fast decoding How to reason about inference bottlenecks in production systems Timestamps: 0:00 - Introduction to prefill and decode 1:05 - What happens during the prefill stage 3:00 - Live example: long vs short prompts 4:10 - Why decode is faster than prefill 6:00 - Measuring prefill and decode in code 8:20 - Prefill time vs prompt length 9:40 - Decode time vs output length 11:00 - Does prompt length affect decode speed This video is essential if you’re building LLM-powered applications, optimizing inference latency, or preparing to evaluate LLM performance in production. This lesson is part of the LLM Engineering and Deployment Certification Program by Ready Tensor. Enroll Now: https://www.readytensor.ai/llm-certif... About Ready Tensor: Ready Tensor helps AI and ML professionals build, evaluate, and showcase intelligent systems through certifications, competitions, and real-world project publications. Learn more: https://www.readytensor.ai/