Random Samples: LLM Meets Cache: From Application to Architecture [June 27, 2025]

Random Samples is a weekly seminar series that bridges the gap between cutting-edge AI research and real-world application. Designed for AI developers, data scientists, and researchers, each episode explores the latest advancements in AI and how they’re being used in production today.

This week’s topic:
LLM Meets Cache: From Application to Architecture

Abstract:
Large Language Models are powerful yet resource-intensive systems that excel across numerous domains, including autonomous agents, complex reasoning, and content generation. However, their computational demands present significant challenges for practical deployment and scalability. Caching serves as a critical optimization strategy for reusing computational results and reducing redundant processing. By storing and retrieving previously computed outputs, cache systems can dramatically improve efficiency while maintaining performance quality. In this presentation, we will explore cache design from two key perspectives:

1. Application-layer agent caching – An agent RAG cache design for efficient retrieval and response generation
2. Model-layer architectural caching – Including quantized KV (Key-Value) cache implementations for memory efficiency

Through examining these two complementary approaches, we’ll demonstrate how strategic caching can make LLM systems both more performant and more practical for real-world applications.

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