sglang

Speed up LLM inference by using RadixAttention to automatically cache and reuse common prompt prefixes. This allows multi-turn conversations and agent workflows to reuse the KV cache for shared system prompts, making each subsequent interaction significantly faster.

Accelerate structured JSON generation by using a serving framework with RadixAttention to cache shared prefixes. This approach optimizes regex and grammar-based outputs, achieving up to 5x faster generation compared to standard inference methods.

Yes, RadixAttention works with existing models by operating as a high-performance serving framework built on torch and transformers. It automatically manages prefix caching for your models to accelerate inference without requiring fundamental architecture changes.

Set up an LLM serving framework by installing sglang with all extras via pip. It efficiently caches repeated tool definitions and system prompts using RadixAttention, dramatically improving performance for agentic workflows that frequently reuse these common prefixes.

LLM inference slows down because standard methods recompute the prompt prefix for every generation request. Using a serving framework with RadixAttention caches and reuses the KV cache for shared prefixes, preventing redundant processing and accelerating regex-constrained outputs.

Yes, you can use sglang for multi-turn customer service chatbots. It intelligently caches the shared system prompt using RadixAttention, ensuring each subsequent user interaction reuses the KV cache to deliver much faster response times.