1. Environment Preparation Download Miniforge 1 wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" Install Miniforge 1 bash Miniforge3-$(uname)-$(uname -m).sh 1 2 echo "export PATH=$HOME/miniforge3/bin:$PATH" >> ~/.bashrc source ~/.bashrc Create the environment 1 conda create -n vllm python=3.12 vLLM currently requires Python 3.9+. Activate the environment 1 conda activate vllm Install dependencies 1 conda install

1. Building the Image To make testing convenient, the model files are packaged into the image here. Download the model 1 2 3 4 git clone https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat cd Qwen1.5-1.8B-Chat && git lfs pull rm -rf .git cd .. Write the Dockerfile 1 2 3 4 5 cat <<EOF > Dockerfile

A token is a unit tightly bound to data. It can be used to measure how much corpus is needed to train a model, and also to measure the input and output length at inference time. 1. What a token is A token can be a whole word, a subword,

1. What Is TensorRT TensorRT is a C++ library mainly used for high-performance inference acceleration on NVIDIA GPUs. It provides both a C++ API and a Python API for integration. The mainstream deep learning frameworks TensorRT supports are: Caffe, where TensorRT can read the prototxt format directly TensorFlow, where the

1. Compiling Models with TensorRT-LLM 1.1 Introduction to TensorRT-LLM When using TensorRT, you usually need to convert the model to ONNX format, then convert the ONNX to TensorRT format, and finally run inference in TensorRT or Triton Server. But this conversion process is not simple, and you often run into