Installation#

Xinference can be installed with pip on Linux, Windows, and macOS. To run models using Xinference, you will need to install the backend corresponding to the type of model you intend to serve.

If you aim to serve all supported models, you can install all the necessary dependencies with a single command:

pip install "xinference[all]"

Note

If you want to serve models in GGML format, it’s advised to install the GGML dependencies manually based on your hardware specifications to enable acceleration. For more details, see the Llama.cpp Backend section.

If you want to install only the necessary backends, here’s a breakdown of how to do it.

Transformers Backend#

PyTorch (transformers) supports the inference of most state-of-art models. It is the default backend for models in PyTorch format:

pip install "xinference[transformers]"

vLLM Backend#

vLLM is a fast and easy-to-use library for LLM inference and serving. Xinference will choose vLLM as the backend to achieve better throughput when the following conditions are met:

The model format is pytorch, gptq or awq.
When the model format is pytorch, the quantization is none.
When the model format is awq, the quantization is Int4.
When the model format is gptq, the quantization is Int3, Int4 or Int8.
The system is Linux and has at least one CUDA device
The model family (for custom models) / model name (for builtin models) is within the list of models supported by vLLM

Currently, supported models include:

llama-2, llama-3, llama-2-chat, llama-3-instruct
baichuan, baichuan-chat, baichuan-2-chat
internlm-16k, internlm-chat-7b, internlm-chat-8k, internlm-chat-20b
mistral-v0.1, mistral-instruct-v0.1, mistral-instruct-v0.2
Yi, Yi-chat
code-llama, code-llama-python, code-llama-instruct
c4ai-command-r-v01, c4ai-command-r-v01-4bit
vicuna-v1.3, vicuna-v1.5
internlm2-chat
qwen-chat
mixtral-instruct-v0.1, mixtral-8x22B-instruct-v0.1
chatglm3, chatglm3-32k, chatglm3-128k
deepseek-chat, deepseek-coder-instruct
qwen1.5-chat, qwen1.5-moe-chat
codeqwen1.5-chat
gemma-it
orion-chat, orion-chat-rag

To install Xinference and vLLM:

pip install "xinference[vllm]"

Llama.cpp Backend#

Xinference supports models in gguf and ggml format via llama-cpp-python. It’s advised to install the llama.cpp-related dependencies manually based on your hardware specifications to enable acceleration.

Initial setup:

pip install xinference

Hardware-Specific installations:

Apple Silicon:

CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

Nvidia cards:

CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python

AMD cards:

CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python

SGLang Backend#

SGLang has a high-performance inference runtime with RadixAttention. It significantly accelerates the execution of complex LLM programs by automatic KV cache reuse across multiple calls. And it also supports other common techniques like continuous batching and tensor parallelism.

Initial setup:

pip install 'xinference[sglang]'