Large language model (LLM) inference can quickly become expensive and slow, especially when serving the same or similar requests repeatedly. As more applications incorporate Artificial Intelligence (AI), organizations often face mounting costs from redundant computations and frustrated users waiting for responses. Smart caching strategies offer a powerful solution by storing and reusing previous results, dramatically reducing both response times and invocation overhead. The right caching approach can cut your model serving costs by up to 90% while delivering sub-millisecond response times for cached queries. In this post, we explore proven caching techniques that can transform your model deployment from a cost center into an efficient, responsive system.
Benefits of caching
Caching in generative AI applications involves storing and reusing previously computed embeddings, tokens, model outputs, or prompts to reduce latency and computational overhead during…
