# SGLang

SGLang is a fast serving framework for large language models and vision language models. It's very similar to TGI and vLLM and comes
with production ready features.

The core features include:
- **Fast Backend Runtime**:
    - efficient serving with RadixAttention for prefix caching
    - zero-overhead CPU scheduler
    - continuous batching, paged attention, tensor parallelism and pipeline parallelism,
    - expert parallelism, structured outputs, chunked prefill, quantization (FP8/INT4/AWQ/GPTQ), and multi-lora batching

- **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.

## Configuration

![sglang](https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/sglang/sglang.png)

- **Max Running Request**: the max number of concurrent requests
- **Max Prefill Tokens** (per batch): the maximum number of tokens that can be processed in a single prefill operation. This controls the batch size for the prefill phase and helps manage memory usage during prompt processing.
- **Chunked prefill size**: sets how many tokens are processed at once during the prefill phase. If a prompt is longer than this value,
it will be split into smaller chunks and processed sequentially to avoid out-of-memory errors during prefill with long prompts.
For example, setting --chunked-prefill-size 4096 means each chunk will have up to 4096 tokens processed at a time. Setting this to -1
means disabling chunked prefill. 
- **Tensor Parallel Size**: the number of GPUs to use for tensor parallelism. This enables model sharding across multiple GPUs
to handle larger models that don't fit on a single GPU. For example, setting this to 2 will split the model across 2 GPUs.
- **KV Cache DType**: the data type used for storing the key-value cache during generation. Options include "auto", "fp8_e5m2",
and "fp8_e4m3". Using lower precision types can reduce memory usage but may slightly impact generation quality.

For more advanced configuration you can pass any of the [Server Arguments that SGlang supports](https://docs.sglang.ai/backend/server_arguments.html)
as container arguments. For example changing the `schedule-policy` to `lpm` would look like this:

![sglang-advanced](https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/sglang/sglang-advanced.png)

## Supported models

SGlang has wide support for large language models, multimodal language models, embedding models and more. We recommend reading the
[supported models](https://docs.sglang.ai/supported_models/generative_models.html) section in the SGLang documentation for a full list.

In the Inference Endpoints UI, by default, any model on the Hugging Face Hub that has a `transformers` tag, can be deployed with SGLang.
This is because SGLang [implements a fallback](https://docs.sglang.ai/supported_models/transformers_fallback.html#transformers-fallback-in-sglang) to use transformers
if SGLang doesn't have their own implementation of a model.

## References

We also recommend reading the [SGLang documentation](https://docs.sglang.ai/) for more in-depth information.

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