SGLang

SGLang is a serving framework for large language models and multimodal models, built for low-latency and high-throughput inference. It targets teams and researchers who need to run open-weight LLMs, embedding models, reward models, or diffusion models in production, from a single GPU box up to large distributed clusters. It's used both as a standalone inference server and as the rollout backend for reinforcement learning and post-training pipelines.

Key features

• RadixAttention: prefix caching that reuses shared KV cache across requests to cut redundant computation.
• Zero-overhead scheduler: a CPU scheduler designed to minimize scheduling overhead in the request pipeline.
• Prefill-decode disaggregation: separates prefill and decode phases for better resource utilization at scale.
• Speculative decoding: includes newer approaches (DFlash, Spec V2) to speed up generation.
• Continuous batching and paged attention: standard techniques for maximizing GPU utilization under variable request loads.
• Parallelism options: tensor, pipeline, expert, and data parallelism for scaling across multiple GPUs and nodes.
• Quantization support: FP4, FP8, INT4, AWQ, and GPTQ for reduced memory footprint and faster inference.
• Multi-LoRA batching: serve multiple LoRA adapters in the same batch.
• Structured outputs: constrained decoding for JSON and other structured formats, backed by a compressed finite state machine.
• Broad model support: Llama, Qwen, DeepSeek, Kimi, GLM, GPT, Gemma, Mistral, embedding models (e5-mistral, gte, mcdse), reward models (Skywork), and diffusion models (WAN, Qwen-Image), with OpenAI API compatibility.
• Hardware coverage: NVIDIA GPUs (GB200/B300/H100/A100/5090), AMD GPUs (MI355/MI300), Intel Xeon CPUs, Google TPUs, and Ascend NPUs.

Ideal use cases

SGLang fits teams deploying open-weight LLMs or multimodal models in production and needing to squeeze out throughput and latency, particularly at scale (multi-GPU or multi-node clusters, mixture-of-experts models like DeepSeek). It's a solid choice if you need day-0 support for newly released open models, structured/JSON output guarantees, or an RL rollout backend, since it's already used by frameworks like verl, slime, and AReaL for post-training.

It also works well if you need to run on non-standard hardware, such as AMD GPUs, TPUs, or Ascend NPUs, without switching serving stacks.

It is less of a fit if you just want to prototype with a hosted API and don't need self-hosted inference, or if your workload is a single small model on a single consumer GPU where a simpler runtime (like plain Hugging Face transformers or llama.cpp) would be easier to set up. Teams without GPU infrastructure or without the operational capacity to manage a serving cluster may find the setup more than they need.

Installation

SGLang is distributed as a Python package and installable via pip. Full details, including hardware-specific instructions, are in the official docs:

pip install sglang

After installing, you can launch a server and send requests through an OpenAI-compatible API. Consult these guides for the full flow:

• Install SGLang for hardware-specific setup (NVIDIA, AMD, TPU, etc.)
• Quick Start for sending your first request
• Backend Tutorial for using the OpenAI-compatible completions API
• Frontend Tutorial for the SGLang frontend language
• Contribution Guide if you want to contribute code

Because hardware and driver requirements vary significantly (CUDA versions, ROCm, TPU runtime, etc.), the install docs are the authoritative source for exact commands per platform.