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cloud.google.com·7h ago
Data Science
Analytics Vidhya·Sep 8·5 min read
The Four Caches in LLM Serving
TL;DR
Caching is essential in large language model (LLM) applications to reduce inference costs and latency by avoiding redundant processing of tokens. Four specific caching strategies are identified to optimize performance.
✦ Why It Matters
Implement caching strategies in your LLM applications today to enhance performance and reduce costs.
Key Takeaways
- Utilize KV caching to avoid recomputing token states during generation.
- Implement prefix caching to reuse shared prompt segments across requests.
- Leverage prompt caching for frequently reused prompt portions in API calls.
- Adopt semantic caching to return previously generated answers for similar queries.
How It Works
KV caching stores Key and Value tensors in memory to prevent recomputation during token generation. Prefix caching identifies shared prompt segments and reuses their KV states, while prompt caching allows API providers to cache common prompt content.
Semantic caching uses embeddings to compare new queries against previously answered questions, enabling the reuse of answers without invoking the LLM.