Accelerating LLM Inference with Prompt Caching for Open‑Source Models on Databricks

This article describes techniques for using prompt caching to improve the inference speed and efficiency of open-source large language models when deployed on Databricks' platform. Prompt caching reduces redundant processing by storing and reusing previously computed tokens from repeated prompt prefixes, thereby decreasing latency and computational costs. The resource likely provides implementation guidance and performance benefits for organizations running open-source LLMs on Databricks infrastructure.

Source: Databricks | 2026-05-22