sglang

To accelerate LLM inference for structured generation, you can use RadixAttention prefix caching to automatically reuse common prompt prefixes. This approach delivers 5-10x speedup for generating JSON, regex, or grammar-constrained outputs by avoiding redundant KV cache computation.

RadixAttention prefix caching for agentic workflows is a technique that intelligently caches and reuses common prompt prefixes across multi-turn conversations. It enables 5x faster inference by retaining KV cache for repeated system prompts and tool definitions.

You can speed up multi-turn conversations in LLM serving by applying RadixAttention to automatically cache and reuse the KV cache of shared prefixes. This eliminates redundant processing of repeated context, significantly reducing latency for subsequent user queries.

Prefix caching works directly with JSON and regex constrained decoding by optimizing performance for these structured outputs. RadixAttention reuses the KV cache of common prefixes, maintaining efficiency while strictly adhering to the specified grammar or regular expression constraints.

The best way to optimize KV cache reuse for repeated system prompts is using RadixAttention prefix caching. It automatically identifies and retains common prompt prefixes across requests, achieving up to 5-10x speedup over traditional inference methods.

You need prefix caching for LLM inference when building agentic workflows or assistants that frequently reuse the same system prompts and tool definitions. It prevents redundant processing of repeated context, making subsequent structured generation and multi-turn queries much faster.