Top AI Open Source Projects to Watch in 2025

A high-performance C/C++ port of the OpenAI Whisper speech recognition model, supporting pure CPU inference and multi-platform deployment.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference, supporting deep learning applications such as computer vision, automatic speech recognition, generative AI, and natural language processing.

PowerInfer is a high-speed large language model inference engine designed for local deployment, leveraging sparse activation and a CPU/GPU hybrid architecture to achieve fast LLM inference on consumer-grade hardware.

GPT4All: An open-source AI chatbot application for running local large language models on any device.

ONNX Runtime: A cross-platform, high-performance machine learning inference and training accelerator.

MindSpore is a full-scenario deep learning framework designed to provide developers with a friendly design, efficient execution, and flexible deployment experience. It supports deployment on the cloud, edge, and device, and provides a rich model library and tools to help AI application development.

llama.cpp is a LLaMA model inference engine written in pure C/C++, designed for high performance and low resource usage.

Oneflow is a deep learning framework designed for production environments. It features automatic parallelism and distributed training.

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework.

LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be distributed, efficient, and fast, suitable for ranking, classification, and other machine learning tasks.

PaddlePaddle, based on the open-source deep learning platform, integrates deep learning training and inference frameworks, model libraries, tool components, and service platforms. It features leading development convenience, ultra-large-scale training capabilities, multi-end and multi-platform deployment capabilities, and full-stack technology autonomy and controllability.

OpenCV is an open-source computer vision, machine learning, and image processing library that provides a rich set of algorithms and tools, widely used in areas such as image recognition, object detection, and video analysis.

Apache MXNet is a flexible and efficient deep learning framework. It supports both imperative and symbolic programming and provides multiple language bindings, including Python, R, Scala, and C++.

An open-source machine learning framework for everyone