sglang

Fast structured generation for LLMs efficiently outputs JSON, regex, or grammar-constrained text using RadixAttention. It optimizes constrained decoding by caching shared prompt prefixes, accelerating production-scale deployments requiring strict output formats.

Yes, SGLang supports production-scale LLM and VLM deployments on various hardware. It provides high-speed serving optimized for constrained decoding, agentic workflows with tool calls, and scenarios requiring extensive prefix sharing across multiple requests.

You launch an LLM inference server for fast structured generation by starting the SGLang server with your target model, such as Llama 3-8B, on a specified port. This enables automatic prefix caching and optimized constrained decoding for your applications.

Yes, this engine supports production-scale LLM and VLM deployments on various hardware. It provides high-speed serving optimized for constrained decoding, agentic workflows with tool calls, and scenarios requiring extensive prefix sharing across multiple requests.

LLM inference is slow when repeatedly processing the same system instructions because the model recomputes the entire prompt each time. Using RadixAttention for automatic prefix caching solves this by reusing previous computations, drastically reducing latency.