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Learn how prompt caching speeds up OSS LLM inference on Databricks, and delivers secure, automatic performance gains.
Large language model (LLM) inference often involves repeated prompts—think of the same system or instruction prompt appearing in thousands of requests. Reprocessing that identical prefix for every call wastes compute cycles, inflates latency, and increases costs.
Prompt caching can be a powerful technique to raise a model’s quality in specific domains without compromising the model’s token throughput. Queries can share a large domain-specific system prompt, with the compute cost of that shared prompt amortized across all those queries. Frontier models, such as Claude, use system prompts that are many thousands of tokens long under the hood. Furthermore, in our recently published research we showed that automated prompt optimization allows open-source models to surpass frontier-model quality for enterprise tasks.