What is KV cache in LLM inference?

It's the stored keys and values from past tokens, letting decode skip recomputes and slash latency—essential for anything beyond toy prompts.

Why does LLM inference slow with long prompts?

Prefill handles the prompt fast (parallel), but decode generates sequentially, rereading growing context via KV cache—memory bandwidth starves as length explodes.

Can we fix the decode bottleneck in LLMs?

Yep—speculative decoding, paged attention, better hardware. Infinite-context AI? Inevitable, transforming chats to full-knowledge companions.

🤖 Large Language Models

Why AI Chats Crawl on Long Prompts: KV Cache, Prefill, and the Decode Trap

That endless wait when you paste a novel into ChatGPT? It's not just 'thinking'—it's LLM inference hitting a memory wall. Here's the inside story on KV cache and why it changes everything.

theAIcatchup Apr 09, 2026 4 min read

Diagram showing KV cache reducing latency in LLM prefill and decode phases

⚡ Key Takeaways

LLM inference splits into prefill (parallel prompt crunch) and decode (sequential generation loop)—decode's where slowdowns hide. 𝕏
KV cache stores keys/values to avoid recomputing history, but growing contexts crush memory bandwidth. 𝕏
Future fixes like custom chips promise blazing inference, enabling always-on, infinite-context AI for everyone. 𝕏

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#KV cache #LLM inference #decode bottleneck #prefill phase

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Originally reported by Towards AI

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