SGLang
App : A product you use directly
A serving engine competing with vLLM, very fast thanks to the RadixAttention prefix cache.
Worth testing against vLLM on your own traffic; the gap depends heavily on model size.
Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.
What it does, concretely
Running a large language model in production raises throughput and latency challenges: each request starts from scratch, even when it shares a long prefix with previous ones. SGLang solves this by caching already-computed attention states through RadixAttention, avoiding redundant computation on every call.
In practice, SGLang is installed via pip or Docker and exposes an OpenAI-compatible API. It supports quantization (FP4, FP8, INT4, AWQ, GPTQ), speculative decoding, continuous batching, tensor/pipeline/expert parallelism, and structured outputs. It runs on NVIDIA GPUs (H100, A100, GB200, B300), AMD GPUs (MI300, MI355), Intel Xeon CPUs, Google TPUs, and Ascend NPUs.
SGLang targets engineering teams deploying open-source models at scale: post-training researchers, AI infrastructure operators, and companies serving millions of requests per day. It is used in production by xAI, NVIDIA, AMD, LinkedIn, Cursor, Oracle Cloud, Google Cloud, Microsoft Azure, AWS, and others, running on more than 400,000 GPUs worldwide.
However, SGLang is not suited for general users without system administration skills. Installation and configuration require familiarity with Linux, Python environments, and GPU management. Users looking for a graphical interface or a fully managed hosted service should look elsewhere.
What it offers
- ·RadixAttention caches already-computed attention prefixes to avoid redundant computation across requests sharing a common context.
- ·The zero-overhead CPU scheduler organizes batches continuously without adding extra CPU-side latency.
- ·Quantization in FP4, FP8, INT4, AWQ, and GPTQ reduces the required GPU memory without changing the API.
- ·Speculative decoding (DFlash, Spec V2) speeds up token generation by anticipating likely next tokens.
- ·Prefill-decode disaggregation separates processing phases to optimize throughput on large clusters.
- ·Structured outputs (JSON, grammars) are generated via a compressed finite state machine with minimal overhead.
- ·Multi-LoRA batching allows serving multiple LoRA adapters simultaneously within a single batch.
- ·OpenAI API compatibility makes integration into existing applications straightforward without modifying client code.
In practice
- Price
- Free
- Commercial use
- Commercial use: yes
- Available on
- linux, docker
- Licence
- Apache-2.0
Security and data
- Rating
- Security: nothing to flag
- Your data
- Data stays local
- Maintenance
- Active
- Last release
- 19 October 2026
Links and repository checked on 25 October 2026 · added on 1 January 2024
How to install SGLang
pip install sglang[all]