[English](../../best_practices/DeepSeek-V3.md)

# DeepSeek-V3/V3.1 模型

## 一、环境准备
### 1.1 支持情况
DeepSeek-V3/V3.1 各量化精度，在下列硬件上部署所需要的最小卡数如下：

|     | WINT4 |
|-----|-----|
|H800 80GB| 8 |

### 1.2 安装fastdeploy

安装流程参考文档 [FastDeploy GPU 安装](../get_started/installation/nvidia_gpu.md)

## 二、如何使用
### 2.1 基础：启动服务
 **示例1：** H800上八卡部署wint4模型16K上下文的服务
```shell
MODEL_PATH=/models/DeepSeek/DeepSeek-V3.1-Terminus-BF16

export FD_DISABLE_CHUNKED_PREFILL=1
export FD_ATTENTION_BACKEND="MLA_ATTN"
export FLAGS_flash_attn_version=3

python -m fastdeploy.entrypoints.openai.api_server \
  --model "$MODEL_PATH" \
  --port 8180 \
  --metrics-port 8181 \
  --engine-worker-queue-port 8182 \
  --cache-queue-port 8183 \
  --tensor-parallel-size 8 \
  --max-model-len  16384 \
  --max-num-seq 100 \
  --no-enable-prefix-caching \
  --quantization wint4

```

**示例2：** H800上16卡部署 block_wise_fp8 模型16K上下文的服务

```shell
MODEL_PATH=models/DeepSeek/DeepSeek-V3.1-Terminus-BF16

export FD_DISABLE_CHUNKED_PREFILL=1
export FD_ATTENTION_BACKEND="MLA_ATTN"
export FLAGS_flash_attn_version=3

# 暂时只支持 tp_size为8，ep_size 为 16的 配置


export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export FD_ENABLE_MULTI_API_SERVER=1
python -m fastdeploy.entrypoints.openai.multi_api_server \
       --ports "9811" \
       --num-servers 1 \
       --args --model "$MODEL_PATH" \
       --ips "10.95.247.24,10.95.244.147" \
       --no-enable-prefix-caching \
       --quantization block_wise_fp8 \
       --disable-sequence-parallel-moe \
       --tensor-parallel-size 8 \
       --num-gpu-blocks-override 1024 \
       --data-parallel-size 2 \
       --max-model-len 16384 \
       --enable-expert-parallel \
       --max-num-seqs 20 \
       --graph-optimization-config '{"use_cudagraph":true}' \

```

**示例3：** H800上16卡部署 block_wise_fp8 模型16K上下文的服务

这个例子中支持使用FlashMLA算子做MLA的计算

```shell
MODEL_PATH=models/DeepSeek/DeepSeek-V3.1-Terminus-BF16
export FD_DISABLE_CHUNKED_PREFILL=1
export FD_ATTENTION_BACKEND="MLA_ATTN"
export FLAGS_flash_attn_version=3
export USE_FLASH_MLA=1

export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export FD_ENABLE_MULTI_API_SERVER=1
python -m fastdeploy.entrypoints.openai.multi_api_server \
       --ports "9811,9812,9813,9814,9815,9816,9817,9818" \
       --num-servers 8 \
       --args --model "$MODEL_PATH" \
       --ips "10.95.246.220,10.95.230.91" \
       --no-enable-prefix-caching \
       --quantization block_wise_fp8 \
       --disable-sequence-parallel-moe \
       --tensor-parallel-size 1 \
       --num-gpu-blocks-override 1024 \
       --data-parallel-size 16 \
       --max-model-len 16384 \
       --enable-expert-parallel \
       --max-num-seqs 20 \
       --graph-optimization-config '{"use_cudagraph":true}' \
```

# DeepSeek-V3.2 Model
## 一、环境准备
### 1.1 支持情况
DeepSeek-V3.2 模型在block_wise_fp8在现量化下，在下列硬件上部署所需要的最小卡数如下：

|     | block_wise_fp8 |
|-----|-----|
|H800 80GB| 16 |

### 1.2 安装fastdeploy

安装流程参考文档 [FastDeploy GPU 安装](../get_started/installation/nvidia_gpu.md)

## 二、如何使用
### 2.1 基础：启动服务
 **示例1：** H800上十六卡部署block_wise_fp8模型8K上下文的服务

```shell
MODEL_PATH=/models/DeepSeek-V3.2-Exp-BF16
export FD_DISABLE_CHUNKED_PREFILL=1
export FD_ATTENTION_BACKEND="DSA_ATTN"
export FD_ENABLE_MULTI_API_SERVER=1

python -m fastdeploy.entrypoints.openai.multi_api_server \
       --ports "8091,8092,8093,8094,8095,8096,8097,8098" \
       --num-servers 8 \
       --args --model "$MODEL_PATH" \
       --ips "10.95.246.79,10.95.239.17" \
       --no-enable-prefix-caching \
       --quantization block_wise_fp8 \
       --disable-sequence-parallel-moe \
       --tensor-parallel-size 1 \
       --gpu-memory-utilization 0.85 \
       --max-num-batched-tokens 8192 \
       --data-parallel-size 16 \
       --max-model-len 8192 \
       --enable-expert-parallel \
       --max-num-seqs 20 \
       --num-gpu-blocks-override 2048 \
       --graph-optimization-config '{"use_cudagraph":false}' \
       --no-enable-overlap-schedule
```
