KV Cache Explained Like You’re an LLM Engineer

2026-05-20 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Advanced, long

Summary

The KV cache is a critical optimization for large language model (LLM) inference, addressing the inherent inefficiency of autoregressive generation where each new token requires recomputing attention over the entire preceding sequence. Without it, a 7B parameter model generating a 200-token response would recompute attention 200 times, making production use impractical. The KV cache stores the Key and Value tensors for all previously processed tokens, eliminating redundant computation. This allows the model to compute Query only for the new token and append new Key/Value pairs to the cache, then run attention against all cached K/V. While prefill is compute-bound, the decode phase becomes memory-bandwidth-bound, as the cache grows linearly with sequence length, consuming significant GPU memory (e.g., 26 GB for LLaMA-2 13B at batch size 8 with 4K context).

Key takeaway

For MLOps Engineers deploying LLMs, understanding KV cache is crucial for optimizing inference performance and cost. Your ability to manage KV cache memory directly impacts concurrent user capacity and Time to First Token (TTFT). Implement strategies like PagedAttention, continuous batching, and prefix caching to maximize GPU utilization and prevent Out of Memory (OOM) errors, especially with long-context models.

Key insights

KV cache makes LLM inference viable by storing past Key/Value tensors, avoiding redundant attention recomputation.

Principles

• Autoregressive generation is sequential and expensive.
• K and V projections are fixed once a token is processed.
• Decode phase is memory-bandwidth-bound.

Method

The KV cache stores Key and Value tensors for processed tokens. At each decode step, new K/V are computed and appended, and attention uses new Q against all cached K/V.

In practice

• Use PagedAttention to reduce KV cache fragmentation.
• Implement prefix caching for common system prompts.
• Consider KV cache quantization for memory reduction.

Topics

• KV Cache Optimization
• Transformer Inference
• PagedAttention
• GPU Memory Management
• Long-Context LLMs

Best for: Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Shipping LLMs (Part 2/6): What’s Actually in Your KV Cache? — The KV cache stores per-token key/value tensors, significantly impacting LLM memory consumption and context window cost.
• Optimize, deploy, and benchmark an open-source LLM with vLLM — Efficient LLM inference is achieved through specialized memory management and serving systems like vLLM.
• KV Cache - Explained — Caching Key and Value vectors eliminates quadratic attention recomputation, making LLM inference linear per step.
• AI 101: From Tokens to Answers: What Actually Happens During LLM Inference — Modern LLM inference is a complex system orchestration problem balancing latency, cost, and resource efficiency across distinct prefill and decode phases.
• LLM Inference and Optimization: Fundamentals, Bottlenecks, and Techniques — Optimizing LLM inference requires understanding distinct prefill and decode phases, leveraging KV caching, and applying advanced techniques.
• KV Caching in LLMs: A Guide for Developers — KV caching reuses attention's key and value projections to accelerate autoregressive LLM inference by avoiding redundant computation.

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.