How do LLM caching systems improve scalability and cost efficiency?

LLM caching reduces redundant computation by reusing previous inference results, embeddings, or attention states.

Inference costs are among the largest operational expenses in production LLM systems. Caching improves efficiency by avoiding repeated computations.

Common cache layers include:

Stores outputs for repeated prompts.

Avoids recomputing embeddings for identical text.

Stores transformer attention states during autoregressive generation.

Caches vector search results.

Uses embedding similarity to match semantically equivalent queries.…