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SGLang Documentation
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====================
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SGLang is a fast serving framework for large language models and vision language models.
It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.
The core features include:
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- **Fast Backend Runtime** : Provides efficient serving with RadixAttention for prefix caching, zero-overhead CPU scheduler, continuous batching, token attention (paged attention), speculative decoding, tensor parallelism, chunked prefill, structured outputs, quantization (FP8/INT4/AWQ/GPTQ), and multi-lora batching.
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- **Flexible Frontend Language** : Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
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- **Extensive Model Support** : Supports a wide range of generative models (Llama, Gemma, Mistral, Qwen, DeepSeek, LLaVA, etc.), embedding models (e5-mistral, gte, mcdse) and reward models (Skywork), with easy extensibility for integrating new models.
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- **Active Community** : SGLang is open-source and backed by an active community with industry adoption.
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.. toctree ::
:maxdepth: 1
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:caption: Installation
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start/install.md
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.. toctree ::
:maxdepth: 1
:caption: Backend Tutorial
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references/deepseek
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references/llama4
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backend/send_request.ipynb
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backend/openai_api_completions.ipynb
backend/openai_api_vision.ipynb
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backend/openai_api_embeddings.ipynb
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backend/native_api.ipynb
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backend/offline_engine_api.ipynb
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.. toctree ::
:maxdepth: 1
:caption: Advanced Backend Configurations
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backend/server_arguments.md
backend/sampling_params.md
backend/hyperparameter_tuning.md
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backend/attention_backend.md
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.. toctree ::
:maxdepth: 1
:caption: Supported Models
supported_models/generative_models.md
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supported_models/multimodal_language_models.md
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supported_models/embedding_models.md
supported_models/reward_models.md
supported_models/support_new_models.md
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.. toctree ::
:maxdepth: 1
:caption: Advanced Features
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backend/speculative_decoding.ipynb
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backend/structured_outputs.ipynb
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backend/function_calling.ipynb
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backend/separate_reasoning.ipynb
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backend/structured_outputs_for_reasoning_models.ipynb
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backend/custom_chat_template.md
backend/quantization.md
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backend/lora.ipynb
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backend/pd_disaggregation.md
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.. toctree ::
:maxdepth: 1
:caption: Frontend Tutorial
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frontend/frontend.ipynb
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frontend/choices_methods.md
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.. toctree ::
:maxdepth: 1
:caption: SGLang Router
router/router.md
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.. toctree ::
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:maxdepth: 1
:caption: References
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references/general
references/hardware
references/advanced_deploy
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references/performance_analysis_and_optimization
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references/developer