vllm and inference
These are competitors—both provide unified inference serving engines for running multiple types of LLMs, but vLLM focuses on high-throughput optimization for a single inference backend, while Xinference abstracts across multiple heterogeneous model types and deployment environments.
About vllm
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
This project helps machine learning engineers and developers efficiently deploy and serve large language models (LLMs) in production environments. You provide your chosen LLM and receive a high-throughput, memory-optimized inference service ready for use. It's designed for ML engineers, MLOps specialists, and developers who need to integrate LLM capabilities into applications without sacrificing speed or cost efficiency.
About inference
xorbitsai/inference
Swap GPT for any LLM by changing a single line of code. Xinference lets you run open-source, speech, and multimodal models on cloud, on-prem, or your laptop — all through one unified, production-ready inference API.
This tool helps AI developers and researchers deploy and manage various artificial intelligence models, including large language models (LLMs), speech recognition, and multimodal models. It takes trained AI models and makes them accessible through a unified API, allowing other applications to easily interact with them. Anyone building AI-powered applications, from chatbots to image analysis tools, would use this to put their models into production.
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