The SGLang and DeepSeek teams collaborated to get DeepSeek V3 FP8 running on NVIDIA and AMD GPUs **from day one**. SGLang also supports [MLA optimization](https://lmsys.org/blog/2024-09-04-sglang-v0-3/#deepseek-multi-head-latent-attention-mla-throughput-optimizations) and [DP attention](https://lmsys.org/blog/2024-12-04-sglang-v0-4/#data-parallelism-attention-for-deepseek-models), making SGLang one of the best open-source LLM engines for running DeepSeek models. SGLang is the inference engine recommended by the official [DeepSeek team](https://github.com/deepseek-ai/DeepSeek-V3/tree/main?tab=readme-ov-file#62-inference-with-sglang-recommended).
Special thanks to Meituan's Search & Recommend Platform Team and Baseten's Model Performance Team for implementing the model, and DataCrunch for providing GPU resources.
For optimizations made on the DeepSeek series models regarding SGLang, please refer to [DeepSeek Model Optimizations in SGLang](https://docs.sglang.ai/references/deepseek.html).
SGLang provides several optimizations specifically designed for the DeepSeek model to boost its inference speed. This document outlines current optimizations for DeepSeek. Additionally, the SGLang team is actively developing enhancements for [DeepSeek V3](https://github.com/sgl-project/sglang/issues/2591).
Special thanks to Meituan's Search & Recommend Platform Team and Baseten's Model Performance Team for implementing the model, and DataCrunch for providing GPU resources.
## Launch DeepSeek V3 with SGLang
SGLang is recognized as one of the top engines for [DeepSeek model inference](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3). To run DeepSeek V3/R1 models, the requirements are as follows:
