DeepGEMM: clean and efficient FP8 GEMM kernels with fine-grained scaling

DeepEP: an efficient expert-parallel communication library

Core Functional Library for Distributed Training

cuDNN Frontend is NVIDIA's modern, open-source entry point to the cuDNN library and a growing collection of high-performance open-source kernels.

Accelerating MoE with IO and Tile-aware Optimizations

PaddleFormers is an easy-to-use library of pre-trained large language model zoo based on PaddlePaddle.

PArallel Distributed Deep LEarning （『飞桨』核心框架，高性能单机、分布式训练和跨平台部署）

Fast and memory-efficient exact attention

Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek, Qwen, Llama, Gemma, TTS 2x faster with 70% less VRAM.

An NLP library with Awesome pre-trained Transformer models and easy-to-use interface, supporting wide-range of NLP tasks from research to industrial applications.

The official repository for ERNIE 4.5 and ERNIEKit – its industrial-grade development toolkit based on PaddlePaddle.

📚[WIP] FFPA: Yet antother Faster Flash Prefill Attention with O(1)⚡️GPU SRAM complexity for headdim > 256, 1.8x~3x↑🎉faster vs SDPA EA.

Paddle Multimodal Integration and eXploration, supporting mainstream multi-modal tasks, including end-to-end large-scale multi-modal pretrain models and diffusion model toolbox. Equipped with high …

A tools to automatically analysis and compare the layer-wise perf.

Paddle Distributed Training Examples. 飞桨分布式训练示例 Resnet Bert GPT MOE DataParallel ModelParallel PipelineParallel HybridParallel AutoParallel Zero Sharding Recompute GradientMerge Offload AMP DGC Loc…

Documentations for PaddlePaddle

Optimized primitives for collective multi-GPU communication

Object detection and instance segmentation toolkit based on PaddlePaddle.

Markdown to reStructuredText converter

Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证的深度学习模型）

Newbie collaboration test

Deep Learning 101 with PaddlePaddle （『飞桨』深度学习框架入门教程）

PaddlePaddle Fluid 版本系列教程，CSDN博客专栏：https://blog.csdn.net/qq_33200967/column/info/28685

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