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A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilizatio…
Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels
MooreThreads / tilelang_musa
Forked from tile-ai/tilelangDomain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels
[HPCA 2026] AI Accelerator Benchmark focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and versatility of software and hardware.
This repository organizes materials, recordings, and schedules related to AI-infra learning meetings.
Based on Nano-vLLM, a simple replication of vLLM with self-contained paged attention and flash attention implementation
MNN: A blazing-fast, lightweight inference engine battle-tested by Alibaba, powering high-performance on-device LLMs and Edge AI.
AISBench Benchmark is a model evaluation tool built on OpenCompass, compatible with OpenCompass’s configuration system, dataset structure, and model backend implementation, while extending support …
An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
High Performance LLM Inference Operator Library
A high-throughput and memory-efficient inference and serving engine for LLMs
The AI that really does things. Any OS. Any Platform. The lobster way. 🦞
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
The Compute Library is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.
Offical PyTorch implementation of "BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework"
JeffreyXiang / nvdiffrec
Forked from NVlabs/nvdiffrecOfficial code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".
Code for 3D object detection for autonomous driving
Native and Compact Structured Latents for 3D Generation
World's first general purpose 3D object detection codebse.
Library for conversion between Traditional and Simplified Chinese
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
pytorch implementation for "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593
FlashMLA: Efficient Multi-head Latent Attention Kernels