Sglang
SGLang is a high-performance serving framework for large language models and multimodal models.
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What is Sglang?
SGLang is an open-source high-performance serving framework for programming and serving large language models (LLMs) and multimodal models (VLMs). It was introduced by researchers from LMSYS and combines a Python-embedded frontend language for structured generation with a backend runtime optimized for high-throughput, low-latency inference. The framework is designed for production-level serving, powering over 400,000 GPUs worldwide and generating trillions of tokens daily.
Key features include a fast backend runtime with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and support for INT4/FP8/AWQ/GPTQ quantization. The flexible frontend language enables programming LLM applications with chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions. SGLang supports a wide range of open models including Llama, Mistral, Qwen, DeepSeek, LLaVA, and embedding models, with compatibility for Hugging Face models and OpenAI-style APIs.
SGLang is built for large-scale deployments, delivering reliable serving from a single GPU to distributed clusters. It offers native support across diverse hardware platforms including NVIDIA GPUs, AMD GPUs (ROCm), Intel Xeon, Google TPU, and Ascend NPUs. The framework is ideal for ML engineers, AI researchers, and development teams building production AI applications, agents, chat systems, RAG pipelines, and enterprise AI solutions requiring high-throughput inference.
Sglang pricing
Pricing model: Freemium
SGLang is completely free and open-source under the LMSYS non-profit organization. There are no paid plans, subscription tiers, or licensing fees. The framework is available via pip installation, Docker, or from source code on GitHub. All features including RadixAttention, quantization, multi-GPU parallelism, and OpenAI-compatible API are included at no cost.
Sglang pros
- State-of-the-art inference performance with RadixAttention prefix caching
- Open-source framework with no licensing costs
- Supports 400,000+ GPUs in production worldwide
- OpenAI-compatible API for drop-in replacement
- Broad model support including Llama, Mistral, Qwen, DeepSeek, LLaVA
- Multi-hardware support: NVIDIA, AMD, Intel, TPU, Ascend NPUs
- Continuous batching for high throughput
- Multiple quantization formats: INT4, FP8, AWQ, GPTQ, INT8, FP4
- Tensor parallelism and pipeline parallelism for multi-GPU部署
- FlashInfer kernels for optimized GPU performance
- Jump-forward constrained decoding for structured outputs
- Chunked prefill for handling long prompts efficiently
- Function calling and tool use support for compatible models
- Comprehensive observability with Prometheus metrics and OpenTelemetry
- Active community with industry adoption and weekly public meetings
- Disaggregated prefill/decode for optimized resource usage
- Speculative decoding for faster inference
- Zero-overhead scheduler for efficient request handling
- Multi-modal input support for vision-language models
- Easy installation via pip, source, or Docker
Sglang cons
- Non-deterministic outputs even with temperature set to 0 due to dynamic batching and prefix caching
- CUDA out of memory errors require manual parameter tuning
- Learning curve for optimizing performance parameters
- CPU support has limited functionality
- TPU support is experimental
- Server hangs can occur from memory, network, or bug issues
- Requires GPU hardware for optimal performance
- Kernel errors may be challenging to resolve without GitHub issues
Frequently asked questions about Sglang
Why are results not deterministic even with temperature set to 0?
Non-determinism arises from dynamic batching (about 95% of the issue) and prefix caching. Different batch sizes cause PyTorch/CuBLAS to dispatch to different CUDA kernels, creating slight numerical differences that accumulate across layers. To achieve more deterministic outputs, use --disable-radix-cache and send one request at a time, or enable deterministic mode with --enable-deterministic-inference.
What models are supported by SGLang?
SGLang supports generative models (LLaMA, Mistral, Qwen, DeepSeek, Gemma 2, GLM), multimodal language models (LLaVA, Qwen-VL), embedding and reranking models (e5-mistral), reward models, and diffusion models (GPT-OSS). It is compatible with most Hugging Face models and has easy extensibility for integrating new models.
How do I optimize SGLang performance?
Optimize by adjusting batch sizes and memory parameters, using appropriate quantization methods (FP8, INT4, AWQ, GPTQ), configuring tensor/pipeline parallelism, tuning chunked prefill sizes, enabling CUDA graphs, and using prefix caching. Refer to the Hyperparameter Tuning guide for detailed recommendations.
Can I use SGLang with multiple GPUs?
Yes, SGLang supports Tensor Parallelism (TP) to split models across GPUs, Pipeline Parallelism (PP) to split layers across GPUs, Data Parallelism (DP) to replicate models for higher throughput, and Expert Parallelism for MoE models. Use --tp, --dp, and --pp arguments to configure parallelism.
How do I integrate SGLang with my application?
SGLang provides multiple integration options: OpenAI-compatible API as a drop-in replacement for OpenAI clients, Native Python API for direct integration in Python applications, Ollama API compatible with Ollama clients, and gRPC for high-performance scenarios. No client code changes are needed with the OpenAI-compatible API.
Does SGLang support function calling and tool use?
Yes, SGLang supports function calling and tool use for compatible models. Use the tools parameter in requests to define available functions. The strictness level can be controlled via the SGLANG_TOOL_STRICT_LEVEL environment variable.
How do I handle vision/multimodal inputs?
For multimodal models, include images in requests using the image_url format. The message content should be an array containing both text and image_url objects with the image URL. This works with models like llava-v1.6-vicuna-7b and other vision-language models.
What quantization methods are supported?
SGLang supports FP8 (W8A8, per-channel and per-token), INT4 (AWQ, GPTQ), INT8, FP4 (NVFP4), and block-wise quantization. These quantization methods help reduce memory usage and improve inference speed while maintaining model quality.
How do I monitor SGLang in production?
SGLang provides comprehensive observability features including Prometheus metrics endpoint, request logging and tracing, OpenTelemetry integration, health check endpoints, and performance profiling tools. Refer to the Observability guide for detailed setup instructions.
Can I use SGLang on platforms other than NVIDIA GPUs?
Yes, SGLang supports AMD GPUs (ROCm), Ascend NPUs (Huawei), Intel GPUs (XPU), CPU (limited functionality), and TPU (experimental). Refer to platform-specific documentation for setup instructions on non-NVIDIA hardware.