sglang

Speed up LLM inference by using RadixAttention for automatic prefix caching, which reuses KV cache for common prefixes in multi-turn conversations and agentic loops without manual configuration. This yields up to 5x faster inference compared to standard serving frameworks.

Yes, SGLang supports structured generation with JSON, regex, and grammar-based constrained decoding. This enables 5x faster inference for tasks requiring specific output formats and agentic workflows with tool calls.

RadixAttention improves prefix caching by automatically reusing the KV cache for common prefixes encountered in few-shot prompting and multi-turn conversations. This automatic reuse leads to substantial speedups without requiring manual cache configuration.

The best way to serve an LLM with automatic prefix caching is using SGLang to launch a server, such as for the Llama 3-8B model on port 30000. RadixAttention handles the caching automatically to accelerate inference.

Yes, you need PyTorch and Transformers installed as dependencies to use SGLang. These frameworks provide the foundational model loading and tensor operations required for high-performance LLM serving with RadixAttention.

Standard serving frameworks lack automatic prefix caching, causing recomputation for repeated system prompts. Using SGLang with RadixAttention caches these common parts, making each subsequent agentic query much faster.