PaddlePaddle, Baidu's deep learning platform, recently announced the official release of its next-generation framework, PaddlePaddle 3.0. This release introduces five core technological innovations, including "dynamic and static unified automatic parallelism," aiming to significantly reduce the cost of large model development and training, and supporting the infrastructure development of the large model era.
As the core infrastructure supporting large model training and inference tasks, PaddlePaddle 3.0 demonstrates excellent performance optimization. The framework already supports multiple mainstream large models, including Wenxin 4.5 and Wenxin X1. Through optimized DeepSeek-R1 full-blood single-machine deployment, it significantly improves throughput by up to double.
In terms of computing speed, PaddlePaddle 3.0, leveraging its innovatively developed neural network compiler CINN, achieves significant performance improvements. The execution speed of some operators has increased by 4 times, and the end-to-end training speed of the model has also increased by 27.4%, significantly shortening the training time of large models.
Regarding hardware adaptation, PaddlePaddle 3.0 introduces a unified multi-chip adaptation solution, supporting over 60 mainstream chips and covering various application scenarios including training clusters, autonomous driving, and smart terminals. Developers only need to write code once to achieve seamless cross-chip migration, significantly reducing hardware adaptation costs by 80%.
The release of PaddlePaddle 3.0 is undoubtedly a technological innovation for deep learning frameworks, providing more efficient and flexible support for the development and deployment of large-scale AI models.