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

Prompt caching reduces redundancy in LLM inference by reusing computation for identical prompts across multiple requests.

• •Eliminates repeated processing of identical system prompts across thousands of requests
• •Provides lower latency by skipping prefill stage on cache hits and higher throughput per model unit
• •Databricks extends prompt caching support to open-source models including GPT-OSS, Gemma, and Llama variants
• •Prompt caches are isolated, volatile memory-only, and automatically managed without customer configuration
• •Real-world production results show 2.5x increase in per-replica input-token throughput and 3x latency reduction at 30% cache hit ratio

This summary was automatically generated by AI based on the original article and may not be fully accurate.