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SGLANG
Fast serving framework for LLMs and multimodal models
Apache-2.0
ABOUT
Production LLM inference is expensive and hard to scale. Existing serving frameworks often struggle with efficient batching, speculative decoding, and handling multimodal workloads under latency constraints. SGLang addresses this by co-designing the runtime with a flexible language for structured generation, enabling advanced scheduling like radix caching, speculative execution, and chunked prefill. The result is significantly higher throughput and lower latency across diverse hardware setups, from a single consumer GPU to large distributed clusters.
INSTALL
pip install sglangINTEGRATION GUIDE
1. Deploy production-grade LLM APIs with state-of-the-art throughput and latency performance
2. Serve multimodal models including vision-language and diffusion models from a unified backend
3. Run RL and post-training inference rollouts for frontier model development at scale
4. Replace vLLM or TGI in existing stacks to improve request-per-second metrics on the same hardware
TAGS
pythoninferenceservingllmmultimodaldistributed