Semantic Caching for LLMs

Semantic caching for large language models stores and reuses previous responses by matching the semantic meaning of queries rather than exact text matches. The system stores hidden-layer representations (embeddings) from the neural network along with the generated outputs in a cache database. When a new query arrives, the system compares its semantic representation to cached queries, and if similar enough, returns the cached response instead of processing through the full model. This can speed up responses by 10× and reduce GPU compute costs by 90% for repeated or similar queries.

The technology addresses the high computational cost of running large language models, which require expensive GPU resources for every inference. By identifying semantically similar queries, the cache can serve appropriate responses even when the exact wording differs. This is particularly valuable for applications with many similar queries, such as customer support, documentation systems, or educational platforms. The semantic matching enables the cache to work effectively even when users phrase questions differently. Available as a web API, the technology can be integrated into existing LLM applications to dramatically improve response times and reduce infrastructure costs while maintaining response quality.