The Complete Guide to Inference Caching in LLMs

2026-04-17 · Source: MachineLearningMastery.com - Machinelearningmastery.com · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Inference caching in large language models (LLMs) significantly reduces cost and latency in production by storing and reusing results of expensive computations. This guide details three main types: KV caching, prefix caching, and semantic caching. KV caching, which is automatic and always on, stores internal attention states (key-value pairs) during a single inference request to avoid recomputing them at each decode step. Prefix caching extends KV caching across multiple requests by storing KV states for shared leading tokens, such as system prompts or reference documents, requiring an exact byte-for-byte match. Semantic caching, an application-side cache, stores complete LLM input/output pairs and retrieves them based on semantic similarity, bypassing the model call entirely for semantically equivalent queries. These strategies are complementary, with KV caching as the foundation, prefix caching offering high leverage for shared prompts, and semantic caching suitable for high-volume, FAQ-style applications.

Key takeaway

For AI Engineers optimizing LLM deployments, prioritize enabling prefix caching for your application's system prompts and shared contexts. This offers the highest immediate cost and latency reduction by reusing KV states across requests. Subsequently, evaluate semantic caching for high-volume applications with repetitive, semantically similar queries to further reduce model calls, ensuring the added embedding and vector search overhead is justified by cache hit rates.

Key insights

Inference caching optimizes LLM performance and cost by reusing computation results across three distinct layers.

Principles

• KV caching is foundational and automatic.
• Prefix caching requires exact prompt prefix matches.
• Semantic caching uses embeddings for similarity matching.

Method

Implement prefix caching for shared system prompts, then add semantic caching if query volume and similarity justify the overhead of embedding and vector search.

In practice

• Place static prompt content first for prefix caching.
• Avoid non-deterministic serialization in prompts.
• Use vector databases for semantic caching.

Topics

• Inference Caching
• KV Caching
• Prefix Caching
• Semantic Caching
• Large Language Models

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• 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.
• Shipping LLMs (Part 1/6): Prompt Caching vs Semantic Caching — Prompt caching is lossless and exact, while semantic caching is lossy and similarity-based, impacting correctness.
• Accelerating LLM Inference with Prompt Caching for Open‑Source Models on Databricks — Prompt caching for LLMs significantly reduces inference latency and increases throughput by reusing identical prompt prefixes.
• CAG vs Long Context: How AI Models Use and Remember Information — Long context and Cache Augmented Generation (CAG) offer distinct strategies for LLM external knowledge access, balancing simplicity, cost, and performance.
• CacheWeaver: Cache-Aware Evidence Ordering for Efficient Grounded RAG Inference — CacheWeaver reorders RAG evidence to maximize prefix cache reuse, significantly reducing LLM prefill latency.
• 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.

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Editorial summary, takeaway, and curation by AIssential. Original article published by MachineLearningMastery.com - Machinelearningmastery.com.