Prefill vs Decode

PerformanceAdvanced

Definition

Two phases of transformer inference: prefill computes attention over the prompt (often heavy compute), while decode generates tokens step-by-step (often memory/KV-cache bound). Optimizing both phases is key for low latency and high throughput.

Why "Prefill vs Decode" Matters in AI

Understanding prefill vs decode is essential for anyone working with artificial intelligence tools and technologies. This performance-related concept helps practitioners optimize AI systems for speed, accuracy, and efficiency. Whether you're a developer, business leader, or AI enthusiast, grasping this concept will help you make better decisions when selecting and using AI tools.

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Related terms

KV Cache (Key‑Value Cache)FlashAttentionLatency (AI Systems)Throughput (TPS)

Frequently Asked Questions

What is Prefill vs Decode?

Two phases of transformer inference: prefill computes attention over the prompt (often heavy compute), while decode generates tokens step-by-step (often memory/KV-cache bound). Optimizing both phases ...

Why is Prefill vs Decode important in AI?

Prefill vs Decode is a advanced concept in the performance domain. Understanding it helps practitioners and users work more effectively with AI systems, make informed tool choices, and stay current with industry developments.

How can I learn more about Prefill vs Decode?

Start with our AI Fundamentals course, explore related terms in our glossary, and stay updated with the latest developments in our AI News section.

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