SGLang is a fast serving framework for large language models and vision language models.

NVIDIA Inference Benchmarks provide recipes in ready-to-use templates for evaluating platform speed. Validate your platform across specific AI use cases across hardware and software combinations.

A Datacenter Scale Distributed Inference Serving Framework

Open Source Continuous Inference Benchmarking Qwen3.5, DeepSeek, GPTOSS - GB200 NVL72 vs MI355X vs B200 vs GB300 NVL72 vs H100 & soon™ TPUv6e/v7/Trainium2/3

Cookbook of SGLang - Recipe

DeepEP: an efficient expert-parallel communication library

FlashInfer: Kernel Library for LLM Serving

Ongoing research training transformer models at scale

A PyTorch Native LLM Training Framework

Tensors and Dynamic neural networks in Python with strong GPU acceleration

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Large scale graph learning on a single machine.

[ICLR 2022] "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication" by Cheng Wan, Youjie Li, Cameron R. Wolfe, Anastasios Kyrillidis, Nam Sung …

📊 A simple command-line utility for querying and monitoring GPU status

Geometric Deep Learning Extension Library for PyTorch

Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)