SGLang QuickStart: Install, Configure, and Serve LLMs via OpenAI API

SGLang is a high-performance serving framework for large language models and multimodal models, built to deliver low-latency and high-throughput inference across everything from a single GPU to distributed clusters. For a broader comparison of self-hosted and cloud LLM hosting options — including Ollama, vLLM, llama-swap, LocalAI, and managed cloud providers — see the LLM hosting guide for 2026 . If you already have apps wired to the OpenAI API shape, SGLang is especially appealing because it can expose OpenAI-compatible endpoints for chat completions and completions, helping you migrate from hosted APIs to self-hosted models with minimal client-side changes. When you need to route requests across multiple backends (llama.cpp, vLLM, SGLang, etc.) with hot-swap and TTL-based unloading, llama-swap provides a transparent proxy layer that keeps a single /v1 URL stable while swapping upstreams on demand. This QuickStart walks through installation (multiple methods), practical configuration