Recommended
Driver >=570.26.00
B200/GB200
vLLM Blackwell + CUDA 12.8
High-throughput LLM serving engine with production-ready Docker configuration
Configuration Summary
Framework
vLLM Blackwell
CUDA Version
12.8
Python Support
3.10, 3.11, 3.12
Min Driver
>=570.26.00
Note: NVIDIA Blackwell GPU (B200/GB200) 专用配置
Install Command
pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu128 && pip install vllm What's in vLLM Blackwell
- NVIDIA Blackwell (B200/GB200) architecture support
- FP4 precision for maximum throughput
- CUDA 12.8 optimizations
- Next-gen tensor core utilization
- Enhanced NVLink support for multi-GPU
Performance: 2-3x performance improvement over Hopper architecture
Requires B200 or GB200 GPU with driver 570+
Best For
Use Cases
- Cutting-edge inference deployments
- Maximum performance on latest NVIDIA hardware
- Large model serving (70B+) with FP4 quantization
- Enterprise AI infrastructure
CUDA 12.8 Advantages
- NVIDIA Blackwell GPUs (B200, GB200)
- Bleeding-edge CUDA features
- Maximum inference performance
Limitations: Limited to newest GPU architectures
Generate Dockerfile
Configuration
Local GPU or CPU environment
NVIDIA Blackwell GPU (B200/GB200) 专用配置
Requires NVIDIA Driver >=570.26.00
Dockerfile
1# syntax=docker/dockerfile:12# ^ Required for BuildKit cache mounts and advanced features34# Generated by DockerFit (https://tools.eastondev.com/docker)5# VLLM blackwell + CUDA 12.8 | Python 3.116# Multi-stage build for optimized image size78# ==============================================================================9# Stage 1: Builder - Install dependencies and compile10# ==============================================================================11FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu24.04 AS builder1213# Build arguments14ARG DEBIAN_FRONTEND=noninteractive1516# Environment variables17ENV PYTHONUNBUFFERED=118ENV PYTHONDONTWRITEBYTECODE=119ENV TORCH_CUDA_ARCH_LIST="8.0;8.6;8.9;9.0;10.0"2021# Install Python 3.11 from deadsnakes PPA (Ubuntu 24.04)22RUN apt-get update && apt-get install -y --no-install-recommends \23 software-properties-common \24 && add-apt-repository -y ppa:deadsnakes/ppa \25 && apt-get update && apt-get install -y --no-install-recommends \26 python3.11 \27 python3.11-venv \28 python3.11-dev \29 build-essential \30 git \31 ninja-build32 && rm -rf /var/lib/apt/lists/*3334# Create virtual environment35ENV VIRTUAL_ENV=/opt/venv36RUN python3.11 -m venv $VIRTUAL_ENV37ENV PATH="$VIRTUAL_ENV/bin:$PATH"3839# Upgrade pip40RUN pip install --no-cache-dir --upgrade pip setuptools wheel4142# Install vLLM with BuildKit cache43# Pre-install packaging for potential source builds44RUN --mount=type=cache,target=/root/.cache/pip \45 pip install packaging && \46 pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu128 && pip install vllm4748# Install project dependencies49COPY requirements.txt .50RUN --mount=type=cache,target=/root/.cache/pip \51 pip install -r requirements.txt5253# ==============================================================================54# Stage 2: Runtime - Minimal production image55# ==============================================================================56FROM nvidia/cuda:12.8.0-cudnn-runtime-ubuntu24.04 AS runtime5758# Labels59LABEL maintainer="Generated by DockerFit"60LABEL version="blackwell"61LABEL description="VLLM blackwell + CUDA 12.8"6263# Environment variables64ENV PYTHONUNBUFFERED=165ENV PYTHONDONTWRITEBYTECODE=166ENV NVIDIA_VISIBLE_DEVICES=all67ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility6869# Install Python 3.11 runtime from deadsnakes PPA (Ubuntu 24.04)70RUN apt-get update && apt-get install -y --no-install-recommends \71 software-properties-common \72 && add-apt-repository -y ppa:deadsnakes/ppa \73 && apt-get update && apt-get install -y --no-install-recommends \74 python3.11 \75 libgomp1 \76 ninja-build77 && apt-get remove -y software-properties-common \78 && apt-get autoremove -y \79 && rm -rf /var/lib/apt/lists/*8081# Create non-root user for security82ARG USERNAME=appuser83ARG USER_UID=100084ARG USER_GID=$USER_UID85RUN groupadd --gid $USER_GID $USERNAME \86 && useradd --uid $USER_UID --gid $USER_GID -m $USERNAME8788# Copy virtual environment from builder89COPY --from=builder --chown=$USERNAME:$USERNAME /opt/venv /opt/venv90ENV VIRTUAL_ENV=/opt/venv91ENV PATH="$VIRTUAL_ENV/bin:$PATH"9293# Set working directory94WORKDIR /app9596# Copy application code97COPY --chown=$USERNAME:$USERNAME . .9899# Switch to non-root user100USER $USERNAME101102# Expose port103EXPOSE 8000104105# Default command106CMD ["python", "main.py"]
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Frequently Asked Questions
What GPU memory do I need for vLLM?
GPU memory requirements depend on your model size:
- • 7B models: 16GB+ (T4, A10G)
- • 13B models: 24GB+ (L4, A10G)
- • 70B models: 80GB+ (A100, H100)
vLLM supports tensor parallelism for multi-GPU deployment.
How do I serve a model with vLLM?
Start a vLLM server with OpenAI-compatible API:
python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3.2-3B-Instruct --port 8000
The server provides a drop-in replacement for OpenAI API.
What is the Blackwell configuration for?
The Blackwell configuration is optimized for NVIDIA's latest B200 and GB200 GPUs:
- • Requires CUDA 12.8+
- • Uses PyTorch nightly builds
- • Supports FP4 precision for maximum throughput