DeepSeek671B使用Ktransformers 部署教程

环境配置

类别	详情
CUDA	12.4
Python	3.10
操作系统	Ubuntu 22.04
ktransformers	0.2.2rc2

硬件配置

组件	型号/规格
CPU	Intel Xeon E5-2686 v4
主板	劲鲨 X99 D8i
内存	256GB
显卡	NVIDIA RTX 3080M（16GB 显存）

运行结果

• 推理速度: 6.1 tokens/s 这套价格不到5千,速度流畅，性价比极高，搭配unsloth动态量化Q2.51效果很好，质量损失很少，对比官网效果都很不错。

步骤 1：环境准备

1. 安装系统依赖

   1.1 配置镜像源

   #更新镜像源
   sudo vim  /etc/apt/sources.list
   #写入镜像地址
   deb http://mirrors.aliyun.com/ubuntu/ jammy main restricted universe multiverse
   deb-src http://mirrors.aliyun.com/ubuntu/ jammy main restricted universe multiverse
   
   deb http://mirrors.aliyun.com/ubuntu/ jammy-security main restricted universe multiverse
   deb-src http://mirrors.aliyun.com/ubuntu/ jammy-security main restricted universe multiverse
   deb http://mirrors.aliyun.com/ubuntu/ jammy-updates main restricted universe multiverse
   deb-src http://mirrors.aliyun.com/ubuntu/ jammy-updates main restricted universe multiverse
   deb http://mirrors.aliyun.com/ubuntu/ jammy-proposed main restricted universe multiverse
   deb-src http://mirrors.aliyun.com/ubuntu/ jammy-proposed main restricted universe multiverse
   deb http://mirrors.aliyun.com/ubuntu/ jammy-backports main restricted universe multiverse
   deb-src http://mirrors.aliyun.com/ubuntu/ jammy-backports main restricted universe multiverse
   
   
   

   1.2 安装 Miniconda

   wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh 
   chmod +x Miniconda3-latest-Linux-x86_64.sh 
   ./Miniconda3-latest-Linux-x86_64.sh 
   conda init 
   source ~/.bashrc
   source ~/miniconda3/bin/activate 
   

   1.3 配置 Conda 清华镜像源

   conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
   conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
   conda config --set show_channel_urls yes
   

   1.4 显卡驱动

   nvidia-smi #查看驱动是否已经安装
   上官方下载驱动https://developer.nvidia.com 或B站搜索怎能安装驱动程序
   

   2. CUDA 工具包安装

   2.1 推荐 CUDA 12.4

   wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
   sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
   wget https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda-repo-ubuntu2204-12-4-local_12.4.0-550.54.14-1_amd64.deb
   sudo dpkg -i cuda-repo-ubuntu2204-12-4-local_12.4.0-550.54.14-1_amd64.deb
   sudo cp /var/cuda-repo-ubuntu2204-12-4-local/cuda-*-keyring.gpg /usr/share/keyrings/
   sudo apt-get update
   sudo apt-get -y install cuda-toolkit-12-4
   

   2.2 配置环境变量

   编辑 ~/.bashrc 文件：

   export PATH=$PATH:/usr/local/cuda/bin
   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu
   export CUDA_PATH=/usr/local/cuda
   

   应用配置：

   source ~/.bashrc
   

   2.3 验证安装

   nvcc -V
   

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步骤 2：创建 Conda 环境

1. 新建 Python 3.10 环境

   sudo apt update && sudo apt upgrade -y #更新系统
   sudo apt-get update sudo apt-get install -y build-essential cmake ninja-build libnuma-dev git
   
   conda create --name ktransformers python=3.10
   conda activate ktransformers
   

2. 安装关键依赖

   conda install -c conda-forge libstdcxx-ng
   pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
   pip3 install packaging ninja cpufeature numpy
   

步骤 3：克隆与编译 KTransformers

有git账号的需要设置一下用户名邮箱，没有也可以直接官网下载对应版本的代码

git clone https://github.com/kvcache-ai/ktransformers.git 
cd ktransformers 
git submodule init && git submodule update
# 编译安装（若支持 NUMA）
export USE_NUMA=1
bash install.sh

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步骤 4：安装 Flash Attention

  #更新pip镜像源
   pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
  # 根据 CUDA 和 PyTorch 版本选择对应 wheel（示例为 CUDA 12.4,Python3.10） 
  pip install https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.0.5/flash_attn-2.6.3+cu124torch2.6-cp310-cp310-linux_x86_64.whl

---

步骤 5：下载模型文件

1. 下载 DeepSeek-R1 配置文件

   https://huggingface.co/deepseek-ai/DeepSeek-R1/tree/main
   下载除.safetensors之外的其他文件，放到DeepSeek-R1-config目录

2. 下载 GGUF 模型文件
   国内网络问题，可以从镜像点下载，翻到目录DeepSeek-R1-GGUF,文件有点大，可以用迅雷会员加速下载
   unsloth/DeepSeek-R1-GGUF at main

---

步骤 6：运行模型

命令行交互模式

python -m ktransformers.local_chat \  
--model_path ./DeepSeek-R1-config \
--gguf_path ./DeepSeek-R1-GGUF \ 
--cpu_infer 16 \  
--max_new_tokens 1000 \ 
--force_think \ 
--use_flash_attn

启动 API 服务

# 支持多GPU配置及通过 `--optimize_config_path` 进行更细粒度的显存卸载设置
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True python3.10 ktransformers/server/main.py \
--model_path /home/sean/DeepSeek-R1-GGUF/DeepSeek-R1-config/ \
    --gguf_path /home/sean/DeepSeek-R1-GGUF/DeepSeek-R1-UD-Q2_K_XL/  \
    --model_name unsloth/DeepSeek-R1-UD-Q2_K_XL \
    --cpu_infer 16 \
    --max_new_tokens 2000 \
    --cache_lens 32768 \
    --total_context 32768 \
    --cache_q4 true \
    --temperature 0.6 \
    --top_p 0.95 \
    --optimize_config_path ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml \
    --force_think \
    --use_cuda_graph \
    --host 0.0.0.0 \
    --port 6688

官方教程https://github.com/kvcache-ai/ktransformers/blob/main/doc/en/install.md