repo_name stringlengths 2 22 | repo_link stringlengths 28 60 | category stringlengths 3 39 ⌀ | github_about_section stringlengths 22 415 | homepage_link stringlengths 14 89 ⌀ |
|---|---|---|---|---|
llvm-project | https://github.com/llvm/llvm-project | compiler | The LLVM Project is a collection of modular and reusable compiler and toolchain technologies. | http://llvm.org |
pytorch | https://github.com/pytorch/pytorch | machine learning framework | Tensors and Dynamic neural networks in Python with strong GPU acceleration | https://pytorch.org |
kubernetes | https://github.com/kubernetes/kubernetes | container orchestration | Production-Grade Container Scheduling and Management | https://kubernetes.io |
tensorflow | https://github.com/tensorflow/tensorflow | machine learning framework | An Open Source Machine Learning Framework for Everyone | https://tensorflow.org |
transformers | https://github.com/huggingface/transformers | multi-purpose library | 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. | https://huggingface.co/transformers |
spark | https://github.com/apache/spark | data processing | Apache Spark - A unified analytics engine for large-scale data processing | https://spark.apache.org |
hhvm | https://github.com/facebook/hhvm | virtual machine | A virtual machine for executing programs written in Hack. | https://hhvm.com |
vllm | https://github.com/vllm-project/vllm | inference engine | A high-throughput and memory-efficient inference and serving engine for LLMs | https://docs.vllm.ai |
elasticsearch | https://github.com/elastic/elasticsearch | search engine | Free and Open Source, Distributed, RESTful Search Engine | https://elastic.co/products/elasticsearch |
numpy | https://github.com/numpy/numpy | scientific computing | The fundamental package for scientific computing with Python. | https://numpy.org |
scipy | https://github.com/scipy/scipy | scientific computing | SciPy library main repository | https://scipy.org |
llama.cpp | https://github.com/ggml-org/llama.cpp | inference engine | LLM inference in C/C++ | https://ggml.ai |
ray | https://github.com/ray-project/ray | multi-purpose library | Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads. | https://ray.io |
sglang | https://github.com/sgl-project/sglang | inference engine | SGLang is a fast serving framework for large language models and vision language models. | https://docs.sglang.ai |
rocm-systems | https://github.com/ROCm/rocm-systems | multi-purpose library | super repo for rocm systems projects | https://amd.com/en/products/software/rocm.html |
jax | https://github.com/jax-ml/jax | scientific computing | Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more | https://docs.jax.dev |
onnxruntime | https://github.com/microsoft/onnxruntime | machine learning interoperability | ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator | https://onnxruntime.ai |
verl | https://github.com/volcengine/verl | reinforcement learning | verl: Volcano Engine Reinforcement Learning for LLMs | https://verl.readthedocs.io |
ollama | https://github.com/ollama/ollama | inference engine | Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models. | https://ollama.com |
triton | https://github.com/triton-lang/triton | parallel computing dsl | Development repository for the Triton language and compiler | https://triton-lang.org |
executorch | https://github.com/pytorch/executorch | model compiler | On-device AI across mobile, embedded and edge for PyTorch | https://executorch.ai |
triton-windows | https://github.com/woct0rdho/triton-windows | parallel computing dsl | Fork of the Triton language and compiler for Windows support and easy installation | null |
trl | https://github.com/huggingface/trl | reinforcement learning | Train transformer language models with reinforcement learning. | http://hf.co/docs/trl |
goose | https://github.com/block/goose | agent | an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM | https://block.github.io/goose |
DeepSpeed | https://github.com/deepspeedai/DeepSpeed | training framework | DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. | https://deepspeed.ai |
numba | https://github.com/numba/numba | compiler | NumPy aware dynamic Python compiler using LLVM | https://numba.pydata.org |
torchrec | https://github.com/meta-pytorch/torchrec | distributed computing | Pytorch domain library for recommendation systems | https://meta-pytorch.org/torchrec/ |
modular | https://github.com/modular/modular | parallel computing | The Modular Platform (includes MAX & Mojo) | https://docs.modular.com |
accelerate | https://github.com/huggingface/accelerate | training framework | A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support. | https://huggingface.co/docs/accelerate |
milvus | https://github.com/milvus-io/milvus | vector database | Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search | https://milvus.io |
onnx | https://github.com/onnx/onnx | machine learning interoperability | Open standard for machine learning interoperability | https://onnx.ai |
modelcontextprotocol | https://github.com/modelcontextprotocol/modelcontextprotocol | mcp | Specification and documentation for the Model Context Protocol | https://modelcontextprotocol.io |
ComfyUI | https://github.com/comfyanonymous/ComfyUI | user interface | The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface. | https://comfy.org |
peft | https://github.com/huggingface/peft | fine tuning | PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. | https://huggingface.co/docs/peft |
flashinfer | https://github.com/flashinfer-ai/flashinfer | gpu kernels | FlashInfer: Kernel Library for LLM Serving | https://flashinfer.ai |
trainer | https://github.com/kubeflow/trainer | null | Distributed AI Model Training and LLM Fine-Tuning on Kubernetes | https://www.kubeflow.org/docs/components/trainer/ |
cutlass | https://github.com/NVIDIA/cutlass | parallel computing | CUDA Templates and Python DSLs for High-Performance Linear Algebra | https://docs.nvidia.com/cutlass/index.html |
aiter | https://github.com/ROCm/aiter | gpu kernels | AI Tensor Engine for ROCm | https://rocm.blogs.amd.com/software-tools-optimization/aiter-ai-tensor-engine/README.html |
wandb | https://github.com/wandb/wandb | ml visualization | The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production. | https://wandb.ai |
ao | https://github.com/pytorch/ao | quantization | PyTorch native quantization and sparsity for training and inference | https://pytorch.org/ao |
Mooncake | https://github.com/kvcache-ai/Mooncake | inference | Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI. | https://kvcache-ai.github.io/Mooncake |
ccache | https://github.com/ccache/ccache | compiler | ccache - a fast compiler cache | https://ccache.dev |
unsloth | https://github.com/unslothai/unsloth | fine tuning | Fine-tuning & Reinforcement Learning for LLMs. Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM. | https://docs.unsloth.ai |
torchtitan | https://github.com/pytorch/torchtitan | training framework | A PyTorch native platform for training generative AI models | https://arxiv.org/abs/2410.06511 |
LMCache | https://github.com/LMCache/LMCache | inference | Supercharge Your LLM with the Fastest KV Cache Layer | https://lmcache.ai |
lapack | https://github.com/Reference-LAPACK/lapack | linear algebra | LAPACK is a library of Fortran subroutines for solving the most commonly occurring problems in numerical linear algebra. | https://netlib.org/lapack |
FlagGems | https://github.com/flagos-ai/FlagGems | null | FlagGems is an operator library for large language models implemented in the Triton Language. | null |
ROCm | https://github.com/ROCm/ROCm | multi-purpose library | AMD ROCm Software - GitHub Home | https://rocm.docs.amd.com |
letta | https://github.com/letta-ai/letta | agent | Letta is the platform for building stateful agents: open AI with advanced memory that can learn and self-improve over time. | https://docs.letta.com |
server | https://github.com/triton-inference-server/server | inference server | The Triton Inference Server provides an optimized cloud and edge inferencing solution. | https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html |
aws-neuron-sdk | https://github.com/aws-neuron/aws-neuron-sdk | sdk | Powering AWS purpose-built machine learning chips. Blazing fast and cost effective, natively integrated into PyTorch and TensorFlow and integrated with your favorite AWS services | https://aws.amazon.com/ai/machine-learning/neuron |
Liger-Kernel | https://github.com/linkedin/Liger-Kernel | kernel examples | Efficient Triton Kernels for LLM Training | https://openreview.net/pdf?id=36SjAIT42G |
tilelang | https://github.com/tile-ai/tilelang | parallel computing dsl | Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels | https://tilelang.com |
metaflow | https://github.com/Netflix/metaflow | container orchestration | Build, Manage and Deploy AI/ML Systems | https://metaflow.org |
litgpt | https://github.com/Lightning-AI/litgpt | null | 20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale. | https://lightning.ai/ |
monarch | https://github.com/meta-pytorch/monarch | distributed computing | PyTorch Single Controller | https://meta-pytorch.org/monarch |
hipBLASLt | https://github.com/AMD-AGI/hipBLASLt | Basic Linear Algebra Subprograms (BLAS) | hipBLASLt is a library that provides general matrix-matrix operations with a flexible API and extends functionalities beyond a traditional BLAS library | https://rocm.docs.amd.com/projects/hipBLASLt |
flash-linear-attention | https://github.com/fla-org/flash-linear-attention | gpu kernels | Efficient implementations of state-of-the-art linear attention models | null |
TensorRT | https://github.com/NVIDIA/TensorRT | inference engine | NVIDIA TensorRT is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT. | https://developer.nvidia.com/tensorrt |
AReal | https://github.com/inclusionAI/AReaL | reinforcement learning | The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible. | https://www.inclusion-ai.org/AReaL |
terminal-bench | https://github.com/laude-institute/terminal-bench | benchmark | A benchmark for LLMs on complicated tasks in the terminal | https://tbench.ai |
warp | https://github.com/NVIDIA/warp | spatial computing | A Python framework for accelerated simulation, data generation and spatial computing. | https://nvidia.github.io/warp |
OpenRLHF | https://github.com/OpenRLHF/OpenRLHF | reinforcement learning | An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL) | https://openrlhf.readthedocs.io |
truss | https://github.com/basetenlabs/truss | inference engine | The simplest way to serve AI/ML models in production | https://truss.baseten.co |
RLinf | https://github.com/RLinf/RLinf | reinforcement learning | RLinf: Reinforcement Learning Infrastructure for Embodied and Agentic AI | https://rlinf.readthedocs.io |
nccl | https://github.com/NVIDIA/nccl | distributed computing | Optimized primitives for collective multi-GPU communication | https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html |
SkyRL | https://github.com/NovaSky-AI/SkyRL | reinforcement learning | SkyRL: A Modular Full-stack RL Library for LLMs | https://docs.skyrl.ai/docs |
ROLL | https://github.com/alibaba/ROLL | reinforcement learning | An Efficient and User-Friendly Scaling Library for Reinforcement Learning with Large Language Models | https://alibaba.github.io/ROLL/ |
lightning-thunder | https://github.com/Lightning-AI/lightning-thunder | model compiler | PyTorch compiler that accelerates training and inference. Get built-in optimizations for performance, memory, parallelism, and easily write your own. | null |
helion | https://github.com/pytorch/helion | parallel computing dsl | A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate. | https://helionlang.com |
ort | https://github.com/pykeio/ort | machine learning interoperability | Fast ML inference & training for ONNX models in Rust | https://ort.pyke.io |
deepinv | https://github.com/deepinv/deepinv | null | DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning | https://deepinv.github.io/deepinv/ |
dstack | https://github.com/dstackai/dstack | container orchestration | dstack is an open-source control plane for running development, training, and inference jobs on GPUs-across hyperscalers, neoclouds, or on-prem. | https://dstack.ai |
doctr | https://github.com/mindee/doctr | null | docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning. | https://mindee.github.io/doctr/ |
SWE-bench | https://github.com/SWE-bench/SWE-bench | benchmark | SWE-bench: Can Language Models Resolve Real-world Github Issues? | https://swebench.com |
torchchat | https://github.com/pytorch/torchchat | null | Run PyTorch LLMs locally on servers, desktop and mobile | null |
mcp-agent | https://github.com/lastmile-ai/mcp-agent | mcp | Build effective agents using Model Context Protocol and simple workflow patterns | null |
prime-rl | https://github.com/PrimeIntellect-ai/prime-rl | reinforcement learning | Agentic RL Training at Scale | null |
cuda-python | https://github.com/NVIDIA/cuda-python | middleware | CUDA Python: Performance meets Productivity | https://nvidia.github.io/cuda-python |
open-instruct | https://github.com/allenai/open-instruct | reinforcement learning | AllenAI's post-training codebase | https://allenai.github.io/open-instruct/ |
openevolve | https://github.com/codelion/openevolve | evolutionary algorithm | Open-source implementation of AlphaEvolve | null |
litserve | https://github.com/Lightning-AI/litserve | null | A minimal Python framework for building custom AI inference servers with full control over logic, batching, and scaling. | https://lightning.ai/litserve |
openzl | https://github.com/facebook/openzl | data compression | A novel data compression framework | https://openzl.org |
torchforge | https://github.com/meta-pytorch/torchforge | reinforcement learning | PyTorch-native post-training at scale | https://meta-pytorch.org/torchforge |
torchcodec | https://github.com/meta-pytorch/torchcodec | null | PyTorch media decoding and encoding | https://meta-pytorch.org/torchcodec/stable/index.html |
quack | https://github.com/Dao-AILab/quack | kernel examples | A Quirky Assortment of CuTe Kernels | null |
Triton-distributed | https://github.com/ByteDance-Seed/Triton-distributed | distributed computing | Distributed Compiler based on Triton for Parallel Systems | https://triton-distributed.readthedocs.io |
ThunderKittens | https://github.com/HazyResearch/ThunderKittens | parallel computing | Tile primitives for speedy kernels | https://hazyresearch.stanford.edu/blog/2024-10-29-tk2 |
kernels | https://github.com/huggingface/kernels | gpu kernels | Load compute kernels from the Hub | null |
GEAK-agent | https://github.com/AMD-AGI/GEAK-agent | agent | It is an LLM-based AI agent, which can write correct and efficient gpu kernels automatically. | null |
ome | https://github.com/sgl-project/ome | container orchestration | OME is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs) | http://docs.sglang.ai/ome |
OLMo-core | https://github.com/allenai/OLMo-core | training framework | PyTorch building blocks for the OLMo ecosystem | https://olmo-core.readthedocs.io/en/latest/ |
mistral-inference | https://github.com/mistralai/mistral-inference | inference engine | Official inference library for Mistral models | https://mistral.ai |
triSYCL | https://github.com/triSYCL/triSYCL | parallel computing | Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group | https://trisycl.github.io/triSYCL/Doxygen/triSYCL/html/index.html |
tritonparse | https://github.com/meta-pytorch/tritonparse | performance testing | TritonParse: A Compiler Tracer, Visualizer, and Reproducer for Triton Kernels | https://meta-pytorch.org/tritonparse |
StreamDiffusion | https://github.com/cumulo-autumn/StreamDiffusion | image generation | StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation | https://arxiv.org/abs/2312.12491 |
reference-kernels | https://github.com/gpu-mode/reference-kernels | kernel examples | Official Problem Sets / Reference Kernels for the GPU MODE Leaderboard! | https://gpumode.com |
hatchet | https://github.com/LLNL/hatchet | performance testing | Graph-indexed Pandas DataFrames for analyzing hierarchical performance data | https://llnl-hatchet.readthedocs.io |
kernelbot | https://github.com/gpu-mode/kernelbot | kernel examples | Write a fast kernel and see how you compare against the best humans and AI on gpumode.com | https://www.gpumode.com |
cutile-python | https://github.com/NVIDIA/cutile-python | parallel computing | cuTile is a programming model for writing parallel kernels for NVIDIA GPUs | https://docs.nvidia.com/cuda/cutile-python |
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