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[Docs] clean up structured outputs docs (#2654)

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Lianmin Zheng
2024-12-29 23:57:16 -08:00
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benchmark/deepseek_v3/README.md
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# DeepSeek V3 Support
The SGLang and DeepSeek teams worked together to get DeepSeek V3 FP8 running on NVIDIA and AMD GPUs **from day one**. SGLang also has supported [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.
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.
## Hardware Recommendation
- 8 x NVIDIA H200 GPUs
If you do not have GPUs with large enough memory, please try multi-node tensor parallelism ([help 1](https://github.com/sgl-project/sglang/blob/637de9e8ce91fd3e92755eb2a842860925954ab1/docs/backend/backend.md?plain=1#L88-L95) [help 2](https://github.com/sgl-project/sglang/blob/637de9e8ce91fd3e92755eb2a842860925954ab1/docs/backend/backend.md?plain=1#L152-L168)). Here is an example serving with [2 H20 node](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-2-h208)
If you do not have GPUs with large enough memory, please try multi-node tensor parallelism. There is an example serving with [2 H20 nodes](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-2-h208) below.
## Installation & Launch
@@ -61,10 +61,10 @@ For example, there are two H20 nodes, each with 8 GPUs. The first node's IP is `
```bash
# node 1
GLOO_SOCKET_IFNAME=eth0 python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --nccl-init 10.0.0.1:5000 --nnodes 2 --node-rank 0 --trust-remote-code
python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --nccl-init 10.0.0.1:5000 --nnodes 2 --node-rank 0 --trust-remote-code
# node 2
GLOO_SOCKET_IFNAME=eth0 python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --nccl-init 10.0.0.1:5000 --nnodes 2 --node-rank 1 --trust-remote-code
python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --nccl-init 10.0.0.1:5000 --nnodes 2 --node-rank 1 --trust-remote-code
```
If you have two H100 nodes, the usage is similar to the aforementioned H20.
@@ -72,9 +72,3 @@ If you have two H100 nodes, the usage is similar to the aforementioned H20.
## DeepSeek V3 Optimization Plan
https://github.com/sgl-project/sglang/issues/2591
## Appendix
SGLang is the inference engine officially recommended by the DeepSeek team.
https://github.com/deepseek-ai/DeepSeek-V3/tree/main?tab=readme-ov-file#62-inference-with-sglang-recommended