PaddlePaddle Framework 3.0 Released: Five Core Innovations for Large Models and Scientific Computing

PaddlePaddle 3.0, launched on April 1 2025, introduces five core innovations—including dynamic‑static unified automatic parallelism, a training‑inference integrated PIR, high‑order automatic differentiation for scientific computing, a one‑stage CINN compiler, and heterogeneous multi‑chip adaptation—that dramatically reduce distributed‑training code, boost performance up to four‑fold, and extend the framework to aerospace, automotive, meteorology and life‑science applications while remaining fully compatible with the 2.0 API.

Deep LearningPaddlePaddleautomatic parallelism

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PaddlePaddle Framework 3.0 Released: Five Core Innovations for Large Models and Scientific Computing

Baidu Geek Talk

Aug 28, 2024 · Artificial Intelligence

How PaddlePaddle 3.0 Simplifies Large‑Model Distributed Training with Automatic Parallelism

This article explains the challenges of scaling large AI models, introduces PaddlePaddle 3.0's four‑dimensional hybrid parallelism and its unified automatic parallel framework, details core concepts such as ProcessMesh and Placements, provides step‑by‑step code examples, and outlines performance‑optimizing strategies like operator fusion and pipeline scheduling.

Distributed TrainingHybrid ParallelPaddlePaddle

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How PaddlePaddle 3.0 Simplifies Large‑Model Distributed Training with Automatic Parallelism

DataFunTalk

May 2, 2023 · Artificial Intelligence

Automatic Parallelism in PaddlePaddle: Architecture, Implementation, and Application Practice

This article presents a comprehensive overview of PaddlePaddle's automatic parallel design for heterogeneous scenarios, covering background motivation, architectural principles, key implementation details, practical usage interfaces, and future outlook, while illustrating concepts with detailed diagrams and examples.

AI frameworksDistributed TrainingPaddlePaddle

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Automatic Parallelism in PaddlePaddle: Architecture, Implementation, and Application Practice

Alibaba Cloud Big Data AI Platform

Apr 24, 2023 · Artificial Intelligence

How Alibaba’s TePDist Automates Distributed Deep Learning for Large Models

Alibaba Cloud’s PAI platform unveils TePDist, an HLO‑based automatic distributed deep‑learning system that decouples strategy search from model code, offers client/server architecture, supports SPMD and pipeline parallelism, delivers high performance on GPT, MoE and other models, and is now open‑source.

AI InfrastructureDistributed Deep LearningHLO IR

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How Alibaba’s TePDist Automates Distributed Deep Learning for Large Models