[ { "repo_name": "flashinfer-bench", "repo_link": "https://github.com/flashinfer-ai/flashinfer-bench", "category": "benchmark", "github_about_section": "Building the Virtuous Cycle for AI-driven LLM Systems", "homepage_link": "https://bench.flashinfer.ai", "github_topic_closest_fit": "benchmark", "contributors_all": 12, "contributors_2025": 11, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 1.0, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "cuJSON", "repo_link": "https://github.com/AutomataLab/cuJSON", "category": "library leveraging parallel compute", "github_about_section": "cuJSON: A Highly Parallel JSON Parser for GPUs", "homepage_link": "https://dl.acm.org/doi/10.1145/3760250.3762222", "github_topic_closest_fit": "json-parser", "contributors_all": 2, "contributors_2025": 2, "contributors_2024": 2, "contributors_2023": 0, "growth_2025_percent": 0, "90-day-contributor-retention-rate": 1.0, "180-day-contributor-retention-rate": 1.0 }, { "repo_name": "triton-runner", "repo_link": "https://github.com/toyaix/triton-runner", "github_about_section": "Multi-Level Triton Runner supporting Python, IR, PTX, and cubin.", "homepage_link": "https://triton-runner.org", "contributors_all": 1, "contributors_2025": 1, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 1.0, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "reference-kernels", "repo_link": "https://github.com/gpu-mode/reference-kernels", "category": "kernel examples", "github_about_section": "Official Problem Sets / Reference Kernels for the GPU MODE Leaderboard!", "homepage_link": "https://gpumode.com", "contributors_all": 16, "contributors_2025": 16, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.8, "180-day-contributor-retention-rate": 0.6666666666666666 }, { "repo_name": "intelliperf", "repo_link": "https://github.com/AMDResearch/intelliperf", "category": "performance testing", "github_about_section": "Automated bottleneck detection and solution orchestration", "homepage_link": "https://arxiv.org/html/2508.20258v1", "github_topic_closest_fit": "profiling", "contributors_all": 7, "contributors_2025": 7, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.8, "180-day-contributor-retention-rate": 0.75 }, { "repo_name": "TritonBench", "repo_link": "https://github.com/thunlp/TritonBench", "category": "benchmark", "github_about_section": "TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators", "homepage_link": "https://arxiv.org/abs/2502.14752", "github_topic_closest_fit": "benchmark", "contributors_all": 3, "contributors_2025": 3, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.6666666666666666, "180-day-contributor-retention-rate": 0.0 }, { "repo_name": "CU2CL", "repo_link": "https://github.com/vtsynergy/CU2CL", "github_about_section": "A prototype CUDA-to-OpenCL source-to-source translator, built on the Clang compiler framework", "homepage_link": "http://chrec.cs.vt.edu/cu2cl", "github_topic_closest_fit": "parallel-programming", "contributors_all": 3, "contributors_2025": 0, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.6666666666666666, "180-day-contributor-retention-rate": 0.6666666666666666 }, { "repo_name": "FTorch", "repo_link": "https://github.com/Cambridge-ICCS/FTorch", "category": "wrapper", "github_about_section": "A library for directly calling PyTorch ML models from Fortran.", "homepage_link": "https://cambridge-iccs.github.io/FTorch", "github_topic_closest_fit": "machine-learning", "contributors_all": 20, "contributors_2025": 11, "contributors_2024": 8, "contributors_2023": 9, "growth_2025_percent": 37, "90-day-contributor-retention-rate": 0.6470588235294118, "180-day-contributor-retention-rate": 0.5625 }, { "repo_name": "composable_kernel", "repo_link": "https://github.com/ROCm/composable_kernel", "category": "gpu kernels", "github_about_section": "Composable Kernel: Performance Portable Programming Model for Machine Learning Tensor Operators", "homepage_link": "https://rocm.docs.amd.com/projects/composable_kernel", "contributors_all": 190, "contributors_2025": 140, "contributors_2024": 58, "contributors_2023": 33, "growth_2025_percent": 141, "90-day-contributor-retention-rate": 0.6180555555555556, "180-day-contributor-retention-rate": 0.5043478260869565 }, { "repo_name": "monarch", "repo_link": "https://github.com/meta-pytorch/monarch", "github_about_section": "PyTorch Single Controller", "homepage_link": "https://meta-pytorch.org/monarch", "contributors_all": 85, "contributors_2025": 85, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.6097560975609756, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "oneDPL", "repo_link": "https://github.com/uxlfoundation/oneDPL", "github_about_section": "oneAPI DPC++ Library (oneDPL)", "homepage_link": "https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/dpc-library.html", "contributors_all": 67, "contributors_2025": 17, "contributors_2024": 29, "contributors_2023": 28, "growth_2025_percent": -41, "90-day-contributor-retention-rate": 0.5970149253731343, "180-day-contributor-retention-rate": 0.5151515151515151 }, { "repo_name": "nixl", "repo_link": "https://github.com/ai-dynamo/nixl", "github_about_section": "NVIDIA Inference Xfer Library (NIXL)", "contributors_all": 78, "contributors_2025": 78, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.5957446808510638, "180-day-contributor-retention-rate": 0.391304347826087 }, { "repo_name": "lightning-thunder", "repo_link": "https://github.com/Lightning-AI/lightning-thunder", "github_about_section": "PyTorch compiler that accelerates training and inference. Get built-in optimizations for performance, memory, parallelism, and easily write your own.", "contributors_all": 76, "contributors_2025": 44, "contributors_2024": 47, "contributors_2023": 29, "growth_2025_percent": -6, "90-day-contributor-retention-rate": 0.582089552238806, "180-day-contributor-retention-rate": 0.5 }, { "repo_name": "Primus-Turbo", "repo_link": "https://github.com/AMD-AGI/Primus-Turbo", "contributors_all": 12, "contributors_2025": 12, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.5714285714285714, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "llvm-project", "repo_link": "https://github.com/llvm/llvm-project", "category": "compiler", "github_about_section": "The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.", "homepage_link": "http://llvm.org", "github_topic_closest_fit": "compiler", "contributors_all": 6680, "contributors_2025": 2378, "contributors_2024": 2130, "contributors_2023": 1920, "growth_2025_percent": 11, "90-day-contributor-retention-rate": 0.543317230273752, "180-day-contributor-retention-rate": 0.49890369370888854 }, { "repo_name": "aiter", "repo_link": "https://github.com/ROCm/aiter", "github_about_section": "AI Tensor Engine for ROCm", "homepage_link": "https://rocm.blogs.amd.com/software-tools-optimization/aiter-ai-tensor-engine/README.html", "contributors_all": 151, "contributors_2025": 145, "contributors_2024": 10, "contributors_2023": 0, "growth_2025_percent": 1350, "90-day-contributor-retention-rate": 0.5352112676056338, "180-day-contributor-retention-rate": 0.3902439024390244 }, { "repo_name": "Triton-distributed", "repo_link": "https://github.com/ByteDance-Seed/Triton-distributed", "github_about_section": "Distributed Compiler based on Triton for Parallel Systems", "homepage_link": "https://triton-distributed.readthedocs.io", "contributors_all": 30, "contributors_2025": 30, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.5238095238095238, "180-day-contributor-retention-rate": 0.6 }, { "repo_name": "SYCL-Docs", "repo_link": "https://github.com/KhronosGroup/SYCL-Docs", "github_about_section": "SYCL Open Source Specification", "homepage_link": "https://khronos.org/sycl", "github_topic_closest_fit": "parallel-programming", "contributors_all": 67, "contributors_2025": 13, "contributors_2024": 20, "contributors_2023": 27, "growth_2025_percent": -35, "90-day-contributor-retention-rate": 0.5223880597014925, "180-day-contributor-retention-rate": 0.45454545454545453 }, { "repo_name": "rocm-systems", "repo_link": "https://github.com/ROCm/rocm-systems", "github_about_section": "super repo for rocm systems projects", "contributors_all": 1032, "contributors_2025": 440, "contributors_2024": 323, "contributors_2023": 204, "growth_2025_percent": 36, "90-day-contributor-retention-rate": 0.5213675213675214, "180-day-contributor-retention-rate": 0.44954128440366975 }, { "repo_name": "rocSOLVER", "repo_link": "https://github.com/ROCm/rocSOLVER", "github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo", "homepage_link": "https://github.com/ROCm/rocm-libraries", "contributors_all": 59, "contributors_2025": 20, "contributors_2024": 23, "contributors_2023": 15, "growth_2025_percent": -13, "90-day-contributor-retention-rate": 0.5172413793103449, "180-day-contributor-retention-rate": 0.5094339622641509 }, { "repo_name": "cuda-python", "repo_link": "https://github.com/NVIDIA/cuda-python", "github_about_section": "CUDA Python: Performance meets Productivity", "homepage_link": "https://nvidia.github.io/cuda-python", "github_topic_closest_fit": "parallel-programming", "contributors_all": 48, "contributors_2025": 41, "contributors_2024": 12, "contributors_2023": 1, "growth_2025_percent": 241, "90-day-contributor-retention-rate": 0.5, "180-day-contributor-retention-rate": 0.375 }, { "repo_name": "RaBitQ", "repo_link": "https://github.com/gaoj0017/RaBitQ", "github_about_section": "[SIGMOD 2024] RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search", "homepage_link": "https://github.com/VectorDB-NTU/RaBitQ-Library", "github_topic_closest_fit": "nearest-neighbor-search", "contributors_all": 2, "contributors_2025": 2, "contributors_2024": 1, "contributors_2023": 0, "growth_2025_percent": 100, "90-day-contributor-retention-rate": 0.5, "180-day-contributor-retention-rate": 0.5 }, { "repo_name": "MIOpen", "repo_link": "https://github.com/ROCm/MIOpen", "github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo", "homepage_link": "https://github.com/ROCm/rocm-libraries", "contributors_all": 204, "contributors_2025": 47, "contributors_2024": 62, "contributors_2023": 44, "growth_2025_percent": -24, "90-day-contributor-retention-rate": 0.49019607843137253, "180-day-contributor-retention-rate": 0.44148936170212766 }, { "repo_name": "modular", "repo_link": "https://github.com/modular/modular", "category": "parallel computing", "github_about_section": "The Modular Platform (includes MAX & Mojo)", "homepage_link": "https://docs.modular.com", "github_topic_closest_fit": "parallel-programming", "contributors_all": 366, "contributors_2025": 222, "contributors_2024": 205, "contributors_2023": 99, "growth_2025_percent": 8, "90-day-contributor-retention-rate": 0.4854368932038835, "180-day-contributor-retention-rate": 0.4681647940074906 }, { "repo_name": "hatchet", "repo_link": "https://github.com/LLNL/hatchet", "category": "performance testing", "github_about_section": "Graph-indexed Pandas DataFrames for analyzing hierarchical performance data", "homepage_link": "https://llnl-hatchet.readthedocs.io", "github_topic_closest_fit": "profiling", "contributors_all": 25, "contributors_2025": 3, "contributors_2024": 6, "contributors_2023": 8, "growth_2025_percent": -50, "90-day-contributor-retention-rate": 0.48, "180-day-contributor-retention-rate": 0.44 }, { "repo_name": "hipBLASLt", "repo_link": "https://github.com/AMD-AGI/hipBLASLt", "category": "Basic Linear Algebra Subprograms (BLAS)", "github_about_section": "hipBLASLt is a library that provides general matrix-matrix operations with a flexible API and extends functionalities beyond a traditional BLAS library", "homepage_link": "https://rocm.docs.amd.com/projects/hipBLASLt", "github_topic_closest_fit": "matrix-multiplication", "contributors_all": 111, "contributors_2025": 69, "contributors_2024": 70, "contributors_2023": 35, "growth_2025_percent": -1, "90-day-contributor-retention-rate": 0.4774774774774775, "180-day-contributor-retention-rate": 0.3577981651376147 }, { "repo_name": "truss", "repo_link": "https://github.com/basetenlabs/truss", "category": "inference engine", "github_about_section": "The simplest way to serve AI/ML models in production", "homepage_link": "https://truss.baseten.co", "github_topic_closest_fit": "inference", "contributors_all": 72, "contributors_2025": 44, "contributors_2024": 30, "contributors_2023": 21, "growth_2025_percent": 46, "90-day-contributor-retention-rate": 0.47540983606557374, "180-day-contributor-retention-rate": 0.4423076923076923 }, { "repo_name": "rocRAND", "repo_link": "https://github.com/ROCm/rocRAND", "github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo", "homepage_link": "https://github.com/ROCm/rocm-libraries", "contributors_all": 85, "contributors_2025": 10, "contributors_2024": 32, "contributors_2023": 26, "growth_2025_percent": -68, "90-day-contributor-retention-rate": 0.47058823529411764, "180-day-contributor-retention-rate": 0.42857142857142855 }, { "repo_name": "rdma-core", "repo_link": "https://github.com/linux-rdma/rdma-core", "github_about_section": "RDMA core userspace libraries and daemons", "contributors_all": 437, "contributors_2025": 58, "contributors_2024": 61, "contributors_2023": 66, "growth_2025_percent": -4, "90-day-contributor-retention-rate": 0.4696261682242991, "180-day-contributor-retention-rate": 0.4262295081967213 }, { "repo_name": "Tensile", "repo_link": "https://github.com/ROCm/Tensile", "github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo", "homepage_link": "https://github.com/ROCm/rocm-libraries", "contributors_all": 137, "contributors_2025": 16, "contributors_2024": 25, "contributors_2023": 22, "growth_2025_percent": -36, "90-day-contributor-retention-rate": 0.46715328467153283, "180-day-contributor-retention-rate": 0.4090909090909091 }, { "repo_name": "executorch", "repo_link": "https://github.com/pytorch/executorch", "category": "model compiler", "github_about_section": "On-device AI across mobile, embedded and edge for PyTorch", "homepage_link": "https://executorch.ai", "contributors_all": 437, "contributors_2025": 267, "contributors_2024": 243, "contributors_2023": 77, "growth_2025_percent": 9, "90-day-contributor-retention-rate": 0.4631578947368421, "180-day-contributor-retention-rate": 0.3905325443786982 }, { "repo_name": "hipCUB", "repo_link": "https://github.com/ROCm/hipCUB", "github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo", "homepage_link": "https://github.com/ROCm/rocm-libraries", "contributors_all": 54, "contributors_2025": 10, "contributors_2024": 19, "contributors_2023": 13, "growth_2025_percent": -47, "90-day-contributor-retention-rate": 0.46296296296296297, "180-day-contributor-retention-rate": 0.46296296296296297 }, { "repo_name": "ROCm", "repo_link": "https://github.com/ROCm/ROCm", "github_about_section": "AMD ROCm Software - GitHub Home", "homepage_link": "https://rocm.docs.amd.com", "contributors_all": 166, "contributors_2025": 67, "contributors_2024": 61, "contributors_2023": 44, "growth_2025_percent": 9, "90-day-contributor-retention-rate": 0.4605263157894737, "180-day-contributor-retention-rate": 0.3880597014925373 }, { "repo_name": "rocPRIM", "repo_link": "https://github.com/ROCm/rocPRIM", "github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo", "homepage_link": "https://github.com/ROCm/rocm-libraries", "contributors_all": 76, "contributors_2025": 12, "contributors_2024": 28, "contributors_2023": 15, "growth_2025_percent": -57, "90-day-contributor-retention-rate": 0.4605263157894737, "180-day-contributor-retention-rate": 0.4605263157894737 }, { "repo_name": "ondemand", "repo_link": "https://github.com/OSC/ondemand", "github_about_section": "Supercomputing. Seamlessly. Open, Interactive HPC Via the Web", "homepage_link": "https://openondemand.org", "github_topic_closest_fit": "hpc", "contributors_all": 117, "contributors_2025": 43, "contributors_2024": 23, "contributors_2023": 21, "growth_2025_percent": 86, "90-day-contributor-retention-rate": 0.45544554455445546, "180-day-contributor-retention-rate": 0.4166666666666667 }, { "repo_name": "hip", "repo_link": "https://github.com/ROCm/hip", "github_about_section": "HIP: C++ Heterogeneous-Compute Interface for Portability", "homepage_link": "https://rocmdocs.amd.com/projects/HIP", "contributors_all": 288, "contributors_2025": 46, "contributors_2024": 31, "contributors_2023": 25, "growth_2025_percent": 48, "90-day-contributor-retention-rate": 0.4423791821561338, "180-day-contributor-retention-rate": 0.36923076923076925 }, { "repo_name": "tilelang", "repo_link": "https://github.com/tile-ai/tilelang", "category": "parallel computing dsl", "github_about_section": "Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels", "homepage_link": "https://tilelang.com", "github_topic_closest_fit": "parallel-programming", "contributors_all": 90, "contributors_2025": 89, "contributors_2024": 1, "contributors_2023": 0, "growth_2025_percent": 8800, "90-day-contributor-retention-rate": 0.4423076923076923, "180-day-contributor-retention-rate": 0.3611111111111111 }, { "repo_name": "rocFFT", "repo_link": "https://github.com/ROCm/rocFFT", "github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo", "homepage_link": "https://github.com/ROCm/rocm-libraries", "contributors_all": 81, "contributors_2025": 17, "contributors_2024": 20, "contributors_2023": 19, "growth_2025_percent": -15, "90-day-contributor-retention-rate": 0.43209876543209874, "180-day-contributor-retention-rate": 0.42857142857142855 }, { "repo_name": "lean4", "repo_link": "https://github.com/leanprover/lean4", "category": "theorem prover", "github_about_section": "Lean 4 programming language and theorem prover", "homepage_link": "https://lean-lang.org", "github_topic_closest_fit": "lean", "contributors_all": 278, "contributors_2025": 110, "contributors_2024": 85, "contributors_2023": 64, "growth_2025_percent": 29, "90-day-contributor-retention-rate": 0.42857142857142855, "180-day-contributor-retention-rate": 0.3905579399141631 }, { "repo_name": "tritonparse", "repo_link": "https://github.com/meta-pytorch/tritonparse", "github_about_section": "TritonParse: A Compiler Tracer, Visualizer, and Reproducer for Triton Kernels", "homepage_link": "https://meta-pytorch.org/tritonparse", "contributors_all": 15, "contributors_2025": 15, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.42857142857142855, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "hipBLAS", "repo_link": "https://github.com/ROCm/hipBLAS", "category": "Basic Linear Algebra Subprograms (BLAS)", "github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo", "github_topic_closest_fit": "matrix-multiplication", "contributors_all": 72, "contributors_2025": 21, "contributors_2024": 24, "contributors_2023": 14, "growth_2025_percent": -12, "90-day-contributor-retention-rate": 0.4027777777777778, "180-day-contributor-retention-rate": 0.4 }, { "repo_name": "rocSPARSE", "repo_link": "https://github.com/ROCm/rocSPARSE", "github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo", "homepage_link": "https://github.com/ROCm/rocm-libraries", "contributors_all": 65, "contributors_2025": 19, "contributors_2024": 24, "contributors_2023": 18, "growth_2025_percent": -20, "90-day-contributor-retention-rate": 0.4, "180-day-contributor-retention-rate": 0.41379310344827586 }, { "repo_name": "triSYCL", "repo_link": "https://github.com/triSYCL/triSYCL", "github_about_section": "Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group", "homepage_link": "https://trisycl.github.io/triSYCL/Doxygen/triSYCL/html/index.html", "github_topic_closest_fit": "parallel-programming", "contributors_all": 31, "contributors_2025": 0, "contributors_2024": 1, "contributors_2023": 3, "growth_2025_percent": -100, "90-day-contributor-retention-rate": 0.3870967741935484, "180-day-contributor-retention-rate": 0.22580645161290322 }, { "repo_name": "hhvm", "repo_link": "https://github.com/facebook/hhvm", "github_about_section": "A virtual machine for executing programs written in Hack.", "homepage_link": "https://hhvm.com", "contributors_all": 2624, "contributors_2025": 692, "contributors_2024": 648, "contributors_2023": 604, "growth_2025_percent": 6, "90-day-contributor-retention-rate": 0.38206785137318255, "180-day-contributor-retention-rate": 0.3457513604018418 }, { "repo_name": "tflite-micro", "repo_link": "https://github.com/tensorflow/tflite-micro", "github_about_section": "Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).", "contributors_all": 111, "contributors_2025": 19, "contributors_2024": 25, "contributors_2023": 31, "growth_2025_percent": -24, "90-day-contributor-retention-rate": 0.38095238095238093, "180-day-contributor-retention-rate": 0.3269230769230769 }, { "repo_name": "cudnn-frontend", "repo_link": "https://github.com/NVIDIA/cudnn-frontend", "category": "parallel computing", "github_about_section": "cudnn_frontend provides a c++ wrapper for the cudnn backend API and samples on how to use it", "homepage_link": "https://developer.nvidia.com/cudnn", "github_topic_closest_fit": "parallel-programming", "contributors_all": 12, "contributors_2025": 6, "contributors_2024": 5, "contributors_2023": 1, "growth_2025_percent": 20, "90-day-contributor-retention-rate": 0.375, "180-day-contributor-retention-rate": 0.2857142857142857 }, { "repo_name": "sglang", "repo_link": "https://github.com/sgl-project/sglang", "category": "inference engine", "github_about_section": "SGLang is a fast serving framework for large language models and vision language models.", "homepage_link": "https://docs.sglang.ai", "github_topic_closest_fit": "inference", "contributors_all": 937, "contributors_2025": 796, "contributors_2024": 189, "contributors_2023": 1, "growth_2025_percent": 321, "90-day-contributor-retention-rate": 0.36363636363636365, "180-day-contributor-retention-rate": 0.2436548223350254 }, { "repo_name": "milvus", "repo_link": "https://github.com/milvus-io/milvus", "category": "vector database", "github_about_section": "Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search", "homepage_link": "https://milvus.io", "github_topic_closest_fit": "vector-search", "contributors_all": 387, "contributors_2025": 95, "contributors_2024": 84, "contributors_2023": 72, "growth_2025_percent": 13, "90-day-contributor-retention-rate": 0.36363636363636365, "180-day-contributor-retention-rate": 0.29859154929577464 }, { "repo_name": "tensorflow", "repo_link": "https://github.com/tensorflow/tensorflow", "category": "machine learning framework", "github_about_section": "An Open Source Machine Learning Framework for Everyone", "homepage_link": "https://tensorflow.org", "github_topic_closest_fit": "machine-learning", "contributors_all": 4618, "contributors_2025": 500, "contributors_2024": 523, "contributors_2023": 630, "growth_2025_percent": -4, "90-day-contributor-retention-rate": 0.3572371315442147, "180-day-contributor-retention-rate": 0.29946642952423297 }, { "repo_name": "helion", "repo_link": "https://github.com/pytorch/helion", "category": "parallel computing dsl", "github_about_section": "A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.", "homepage_link": "https://helionlang.com", "github_topic_closest_fit": "parallel-programming", "contributors_all": 49, "contributors_2025": 49, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.35714285714285715, "180-day-contributor-retention-rate": 1.0 }, { "repo_name": "jax", "repo_link": "https://github.com/jax-ml/jax", "category": "scientific computing", "github_about_section": "Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more", "homepage_link": "https://docs.jax.dev", "github_topic_closest_fit": "scientific-computing", "contributors_all": 997, "contributors_2025": 312, "contributors_2024": 280, "contributors_2023": 202, "growth_2025_percent": 11, "90-day-contributor-retention-rate": 0.35538954108858056, "180-day-contributor-retention-rate": 0.3171007927519819 }, { "repo_name": "kubernetes", "repo_link": "https://github.com/kubernetes/kubernetes", "category": "container orchestration", "github_about_section": "Production-Grade Container Scheduling and Management", "homepage_link": "https://kubernetes.io", "github_topic_closest_fit": "kubernetes", "contributors_all": 5041, "contributors_2025": 509, "contributors_2024": 498, "contributors_2023": 565, "growth_2025_percent": 2, "90-day-contributor-retention-rate": 0.35439449169704335, "180-day-contributor-retention-rate": 0.2948559670781893 }, { "repo_name": "pocl", "repo_link": "https://github.com/pocl/pocl", "github_about_section": "pocl - Portable Computing Language", "homepage_link": "https://portablecl.org", "github_topic_closest_fit": "parallel-programming", "contributors_all": 166, "contributors_2025": 26, "contributors_2024": 27, "contributors_2023": 21, "growth_2025_percent": -3, "90-day-contributor-retention-rate": 0.3496932515337423, "180-day-contributor-retention-rate": 0.30625 }, { "repo_name": "server", "repo_link": "https://github.com/triton-inference-server/server", "github_about_section": "The Triton Inference Server provides an optimized cloud and edge inferencing solution.", "homepage_link": "https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html", "github_topic_closest_fit": "inference", "contributors_all": 147, "contributors_2025": 24, "contributors_2024": 36, "contributors_2023": 34, "growth_2025_percent": -33, "90-day-contributor-retention-rate": 0.3401360544217687, "180-day-contributor-retention-rate": 0.3129251700680272 }, { "repo_name": "Vulkan-Tools", "repo_link": "https://github.com/KhronosGroup/Vulkan-Tools", "category": "graphics api", "github_about_section": "Vulkan Development Tools", "homepage_link": "https://vulkan.org", "github_topic_closest_fit": "vulkan", "contributors_all": 248, "contributors_2025": 20, "contributors_2024": 24, "contributors_2023": 24, "growth_2025_percent": -16, "90-day-contributor-retention-rate": 0.33884297520661155, "180-day-contributor-retention-rate": 0.3181818181818182 }, { "repo_name": "ao", "repo_link": "https://github.com/pytorch/ao", "github_about_section": "PyTorch native quantization and sparsity for training and inference", "homepage_link": "https://pytorch.org/ao", "github_topic_closest_fit": "quantization", "contributors_all": 178, "contributors_2025": 114, "contributors_2024": 100, "contributors_2023": 5, "growth_2025_percent": 14, "90-day-contributor-retention-rate": 0.33774834437086093, "180-day-contributor-retention-rate": 0.3 }, { "repo_name": "ThunderKittens", "repo_link": "https://github.com/HazyResearch/ThunderKittens", "category": "parallel computing", "github_about_section": "Tile primitives for speedy kernels", "homepage_link": "https://hazyresearch.stanford.edu/blog/2024-10-29-tk2", "github_topic_closest_fit": "parallel-programming", "contributors_all": 34, "contributors_2025": 29, "contributors_2024": 13, "contributors_2023": 0, "growth_2025_percent": 123, "90-day-contributor-retention-rate": 0.3333333333333333, "180-day-contributor-retention-rate": 0.3333333333333333 }, { "repo_name": "OpenCL-SDK", "repo_link": "https://github.com/KhronosGroup/OpenCL-SDK", "github_about_section": "OpenCL SDK", "homepage_link": "https://khronos.org/opencl", "github_topic_closest_fit": "parallel-programming", "contributors_all": 25, "contributors_2025": 8, "contributors_2024": 6, "contributors_2023": 9, "growth_2025_percent": 33, "90-day-contributor-retention-rate": 0.3333333333333333, "180-day-contributor-retention-rate": 0.34782608695652173 }, { "repo_name": "Self-Forcing", "repo_link": "https://github.com/guandeh17/Self-Forcing", "category": "video generation", "github_about_section": "Official codebase for \"Self Forcing: Bridging Training and Inference in Autoregressive Video Diffusion\" (NeurIPS 2025 Spotlight)", "homepage_link": "https://self-forcing.github.io", "github_topic_closest_fit": "diffusion-models", "contributors_all": 4, "contributors_2025": 4, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.3333333333333333, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "streamv2v", "repo_link": "https://github.com/Jeff-LiangF/streamv2v", "category": "video generation", "github_about_section": "Official Pytorch implementation of StreamV2V.", "homepage_link": "https://jeff-liangf.github.io/projects/streamv2v", "github_topic_closest_fit": "diffusion-models", "contributors_all": 7, "contributors_2025": 3, "contributors_2024": 6, "contributors_2023": 0, "growth_2025_percent": -50, "90-day-contributor-retention-rate": 0.3333333333333333, "180-day-contributor-retention-rate": 0.16666666666666666 }, { "repo_name": "pytorch", "repo_link": "https://github.com/pytorch/pytorch", "category": "machine learning framework", "github_about_section": "Tensors and Dynamic neural networks in Python with strong GPU acceleration", "homepage_link": "https://pytorch.org", "github_topic_closest_fit": "machine-learning", "contributors_all": 5434, "contributors_2025": 1187, "contributors_2024": 1090, "contributors_2023": 1024, "growth_2025_percent": 8, "90-day-contributor-retention-rate": 0.33145302470336513, "180-day-contributor-retention-rate": 0.2921212121212121 }, { "repo_name": "warp", "repo_link": "https://github.com/NVIDIA/warp", "category": "spatial computing", "github_about_section": "A Python framework for accelerated simulation, data generation and spatial computing.", "homepage_link": "https://nvidia.github.io/warp", "github_topic_closest_fit": "physics-simulation", "contributors_all": 79, "contributors_2025": 40, "contributors_2024": 29, "contributors_2023": 17, "growth_2025_percent": 37, "90-day-contributor-retention-rate": 0.32857142857142857, "180-day-contributor-retention-rate": 0.2833333333333333 }, { "repo_name": "triton", "repo_link": "https://github.com/triton-lang/triton", "category": "parallel computing dsl", "github_about_section": "Development repository for the Triton language and compiler", "homepage_link": "https://triton-lang.org", "github_topic_closest_fit": "parallel-programming", "contributors_all": 522, "contributors_2025": 233, "contributors_2024": 206, "contributors_2023": 159, "growth_2025_percent": 13, "90-day-contributor-retention-rate": 0.3236607142857143, "180-day-contributor-retention-rate": 0.27791563275434245 }, { "repo_name": "torchtitan", "repo_link": "https://github.com/pytorch/torchtitan", "github_about_section": "A PyTorch native platform for training generative AI models", "contributors_all": 145, "contributors_2025": 119, "contributors_2024": 43, "contributors_2023": 1, "growth_2025_percent": 176, "90-day-contributor-retention-rate": 0.3229166666666667, "180-day-contributor-retention-rate": 0.3424657534246575 }, { "repo_name": "nccl", "repo_link": "https://github.com/NVIDIA/nccl", "github_about_section": "Optimized primitives for collective multi-GPU communication", "homepage_link": "https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html", "contributors_all": 51, "contributors_2025": 7, "contributors_2024": 5, "contributors_2023": 6, "growth_2025_percent": 40, "90-day-contributor-retention-rate": 0.32, "180-day-contributor-retention-rate": 0.24489795918367346 }, { "repo_name": "metaflow", "repo_link": "https://github.com/Netflix/metaflow", "github_about_section": "Build, Manage and Deploy AI/ML Systems", "homepage_link": "https://metaflow.org", "contributors_all": 121, "contributors_2025": 37, "contributors_2024": 35, "contributors_2023": 28, "growth_2025_percent": 5, "90-day-contributor-retention-rate": 0.3185840707964602, "180-day-contributor-retention-rate": 0.2761904761904762 }, { "repo_name": "terminal-bench", "repo_link": "https://github.com/laude-institute/terminal-bench", "category": "benchmark", "github_about_section": "A benchmark for LLMs on complicated tasks in the terminal", "homepage_link": "https://tbench.ai", "github_topic_closest_fit": "benchmark", "contributors_all": 96, "contributors_2025": 96, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.3181818181818182, "180-day-contributor-retention-rate": 0.29411764705882354 }, { "repo_name": "ray", "repo_link": "https://github.com/ray-project/ray", "github_about_section": "Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.", "homepage_link": "https://ray.io", "contributors_all": 1381, "contributors_2025": 397, "contributors_2024": 223, "contributors_2023": 230, "growth_2025_percent": 78, "90-day-contributor-retention-rate": 0.31763766959297685, "180-day-contributor-retention-rate": 0.25989672977624784 }, { "repo_name": "TensorRT", "repo_link": "https://github.com/NVIDIA/TensorRT", "github_about_section": "NVIDIA TensorRT is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.", "homepage_link": "https://developer.nvidia.com/tensorrt", "contributors_all": 104, "contributors_2025": 10, "contributors_2024": 18, "contributors_2023": 19, "growth_2025_percent": -44, "90-day-contributor-retention-rate": 0.31683168316831684, "180-day-contributor-retention-rate": 0.26 }, { "repo_name": "spark", "repo_link": "https://github.com/apache/spark", "github_about_section": "Apache Spark - A unified analytics engine for large-scale data processing", "homepage_link": "https://spark.apache.org", "github_topic_closest_fit": "big-data", "contributors_all": 3083, "contributors_2025": 322, "contributors_2024": 300, "contributors_2023": 336, "growth_2025_percent": 7, "90-day-contributor-retention-rate": 0.3159986750579662, "180-day-contributor-retention-rate": 0.2586496472959355 }, { "repo_name": "AdaptiveCpp", "repo_link": "https://github.com/AdaptiveCpp/AdaptiveCpp", "github_about_section": "Compiler for multiple programming models (SYCL, C++ standard parallelism, HIP/CUDA) for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!", "homepage_link": "https://adaptivecpp.github.io", "contributors_all": 93, "contributors_2025": 32, "contributors_2024": 32, "contributors_2023": 24, "growth_2025_percent": 0, "90-day-contributor-retention-rate": 0.3146067415730337, "180-day-contributor-retention-rate": 0.3023255813953488 }, { "repo_name": "vllm", "repo_link": "https://github.com/vllm-project/vllm", "category": "inference engine", "github_about_section": "A high-throughput and memory-efficient inference and serving engine for LLMs", "homepage_link": "https://docs.vllm.ai", "github_topic_closest_fit": "inference", "contributors_all": 1885, "contributors_2025": 1369, "contributors_2024": 579, "contributors_2023": 145, "growth_2025_percent": 136, "90-day-contributor-retention-rate": 0.30577223088923555, "180-day-contributor-retention-rate": 0.2512315270935961 }, { "repo_name": "LMCache", "repo_link": "https://github.com/LMCache/LMCache", "github_about_section": "Supercharge Your LLM with the Fastest KV Cache Layer", "homepage_link": "https://lmcache.ai", "contributors_all": 152, "contributors_2025": 144, "contributors_2024": 18, "contributors_2023": 0, "growth_2025_percent": 700, "90-day-contributor-retention-rate": 0.30526315789473685, "180-day-contributor-retention-rate": 0.3235294117647059 }, { "repo_name": "lapack", "repo_link": "https://github.com/Reference-LAPACK/lapack", "category": "linear algebra", "github_about_section": "LAPACK is a library of Fortran subroutines for solving the most commonly occurring problems in numerical linear algebra.", "homepage_link": "https://netlib.org/lapack", "github_topic_closest_fit": "linear-algebra", "contributors_all": 178, "contributors_2025": 20, "contributors_2024": 24, "contributors_2023": 42, "growth_2025_percent": -16, "90-day-contributor-retention-rate": 0.29310344827586204, "180-day-contributor-retention-rate": 0.23837209302325582 }, { "repo_name": "Mooncake", "repo_link": "https://github.com/kvcache-ai/Mooncake", "github_about_section": "Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI.", "homepage_link": "https://kvcache-ai.github.io/Mooncake", "github_topic_closest_fit": "inference", "contributors_all": 138, "contributors_2025": 133, "contributors_2024": 13, "contributors_2023": 0, "growth_2025_percent": 923, "90-day-contributor-retention-rate": 0.2894736842105263, "180-day-contributor-retention-rate": 0.2631578947368421 }, { "repo_name": "onnx", "repo_link": "https://github.com/onnx/onnx", "category": "machine learning interoperability", "github_about_section": "Open standard for machine learning interoperability", "homepage_link": "https://onnx.ai", "github_topic_closest_fit": "onnx", "contributors_all": 370, "contributors_2025": 56, "contributors_2024": 45, "contributors_2023": 61, "growth_2025_percent": 24, "90-day-contributor-retention-rate": 0.28888888888888886, "180-day-contributor-retention-rate": 0.2507204610951009 }, { "repo_name": "elasticsearch", "repo_link": "https://github.com/elastic/elasticsearch", "category": "search engine", "github_about_section": "Free and Open Source, Distributed, RESTful Search Engine", "homepage_link": "https://elastic.co/products/elasticsearch", "github_topic_closest_fit": "search-engine", "contributors_all": 2297, "contributors_2025": 316, "contributors_2024": 284, "contributors_2023": 270, "growth_2025_percent": 11, "90-day-contributor-retention-rate": 0.2855227882037534, "180-day-contributor-retention-rate": 0.2478399272396544 }, { "repo_name": "ome", "repo_link": "https://github.com/sgl-project/ome", "github_about_section": "OME is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs)", "homepage_link": "http://docs.sglang.ai/ome", "github_topic_closest_fit": "k8s", "contributors_all": 28, "contributors_2025": 28, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.2777777777777778, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "Liger-Kernel", "repo_link": "https://github.com/linkedin/Liger-Kernel", "category": "kernel examples", "github_about_section": "Efficient Triton Kernels for LLM Training", "homepage_link": "https://openreview.net/pdf?id=36SjAIT42G", "github_topic_closest_fit": "triton", "contributors_all": 120, "contributors_2025": 78, "contributors_2024": 61, "contributors_2023": 0, "growth_2025_percent": 27, "90-day-contributor-retention-rate": 0.2765957446808511, "180-day-contributor-retention-rate": 0.18421052631578946 }, { "repo_name": "roctracer", "repo_link": "https://github.com/ROCm/roctracer", "github_about_section": "[DEPRECATED] Moved to ROCm/rocm-systems repo", "homepage_link": "https://github.com/ROCm/rocm-systems", "contributors_all": 45, "contributors_2025": 8, "contributors_2024": 11, "contributors_2023": 6, "growth_2025_percent": -27, "90-day-contributor-retention-rate": 0.2727272727272727, "180-day-contributor-retention-rate": 0.2682926829268293 }, { "repo_name": "Vulkan-Docs", "repo_link": "https://github.com/KhronosGroup/Vulkan-Docs", "category": "graphics api", "github_about_section": "The Vulkan API Specification and related tools", "homepage_link": "https://vulkan.org", "github_topic_closest_fit": "vulkan", "contributors_all": 141, "contributors_2025": 18, "contributors_2024": 21, "contributors_2023": 34, "growth_2025_percent": -14, "90-day-contributor-retention-rate": 0.27205882352941174, "180-day-contributor-retention-rate": 0.19402985074626866 }, { "repo_name": "DeepSpeed", "repo_link": "https://github.com/deepspeedai/DeepSpeed", "github_about_section": "DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.", "homepage_link": "https://deepspeed.ai", "contributors_all": 442, "contributors_2025": 96, "contributors_2024": 134, "contributors_2023": 165, "growth_2025_percent": -28, "90-day-contributor-retention-rate": 0.2621359223300971, "180-day-contributor-retention-rate": 0.21212121212121213 }, { "repo_name": "numba", "repo_link": "https://github.com/numba/numba", "github_about_section": "NumPy aware dynamic Python compiler using LLVM", "homepage_link": "https://numba.pydata.org", "contributors_all": 430, "contributors_2025": 36, "contributors_2024": 32, "contributors_2023": 55, "growth_2025_percent": 12, "90-day-contributor-retention-rate": 0.2589073634204275, "180-day-contributor-retention-rate": 0.2028985507246377 }, { "repo_name": "jupyterlab", "repo_link": "https://github.com/jupyterlab/jupyterlab", "category": "user interface", "github_about_section": "JupyterLab computational environment.", "homepage_link": "https://jupyterlab.readthedocs.io", "github_topic_closest_fit": "jupyter", "contributors_all": 698, "contributors_2025": 77, "contributors_2024": 85, "contributors_2023": 100, "growth_2025_percent": -9, "90-day-contributor-retention-rate": 0.25735294117647056, "180-day-contributor-retention-rate": 0.22388059701492538 }, { "repo_name": "ort", "repo_link": "https://github.com/pytorch/ort", "github_about_section": "Accelerate PyTorch models with ONNX Runtime", "contributors_all": 47, "contributors_2025": 0, "contributors_2024": 7, "contributors_2023": 9, "growth_2025_percent": -100, "90-day-contributor-retention-rate": 0.2553191489361702, "180-day-contributor-retention-rate": 0.1702127659574468 }, { "repo_name": "scipy", "repo_link": "https://github.com/scipy/scipy", "category": "scientific computing", "github_about_section": "SciPy library main repository", "homepage_link": "https://scipy.org", "github_topic_closest_fit": "scientific-computing", "contributors_all": 1973, "contributors_2025": 210, "contributors_2024": 251, "contributors_2023": 245, "growth_2025_percent": -16, "90-day-contributor-retention-rate": 0.2542901716068643, "180-day-contributor-retention-rate": 0.2216931216931217 }, { "repo_name": "torchdynamo", "repo_link": "https://github.com/pytorch/torchdynamo", "github_about_section": "A Python-level JIT compiler designed to make unmodified PyTorch programs faster.", "contributors_all": 63, "contributors_2025": 0, "contributors_2024": 1, "contributors_2023": 4, "growth_2025_percent": -100, "90-day-contributor-retention-rate": 0.25396825396825395, "180-day-contributor-retention-rate": 0.09523809523809523 }, { "repo_name": "cutlass", "repo_link": "https://github.com/NVIDIA/cutlass", "category": "parallel computing", "github_about_section": "CUDA Templates and Python DSLs for High-Performance Linear Algebra", "homepage_link": "https://docs.nvidia.com/cutlass/index.html", "github_topic_closest_fit": "parallel-programming", "contributors_all": 238, "contributors_2025": 94, "contributors_2024": 64, "contributors_2023": 66, "growth_2025_percent": 46, "90-day-contributor-retention-rate": 0.25118483412322273, "180-day-contributor-retention-rate": 0.2393617021276596 }, { "repo_name": "goose", "repo_link": "https://github.com/block/goose", "category": "agent", "github_about_section": "an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM", "homepage_link": "https://block.github.io/goose", "github_topic_closest_fit": "ai-agents", "contributors_all": 332, "contributors_2025": 319, "contributors_2024": 32, "contributors_2023": 0, "growth_2025_percent": 896, "90-day-contributor-retention-rate": 0.25, "180-day-contributor-retention-rate": 0.2540983606557377 }, { "repo_name": "dstack", "repo_link": "https://github.com/dstackai/dstack", "category": "gpu provisioning and orchestration", "github_about_section": "dstack is an open-source control plane for running development, training, and inference jobs on GPUs-across hyperscalers, neoclouds, or on-prem.", "homepage_link": "https://dstack.ai", "github_topic_closest_fit": "orchestration", "contributors_all": 69, "contributors_2025": 28, "contributors_2024": 42, "contributors_2023": 14, "growth_2025_percent": -33, "90-day-contributor-retention-rate": 0.25, "180-day-contributor-retention-rate": 0.2545454545454545 }, { "repo_name": "KernelBench", "repo_link": "https://github.com/ScalingIntelligence/KernelBench", "category": "benchmark", "github_about_section": "KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems", "homepage_link": "https://scalingintelligence.stanford.edu/blogs/kernelbench", "github_topic_closest_fit": "benchmark", "contributors_all": 19, "contributors_2025": 16, "contributors_2024": 3, "contributors_2023": 0, "growth_2025_percent": 433, "90-day-contributor-retention-rate": 0.25, "180-day-contributor-retention-rate": 0.2 }, { "repo_name": "kernels", "repo_link": "https://github.com/huggingface/kernels", "category": "gpu kernels", "github_about_section": "Load compute kernels from the Hub", "contributors_all": 15, "contributors_2025": 14, "contributors_2024": 2, "contributors_2023": 0, "growth_2025_percent": 600, "90-day-contributor-retention-rate": 0.25, "180-day-contributor-retention-rate": 0.3333333333333333 }, { "repo_name": "ZLUDA", "repo_link": "https://github.com/vosen/ZLUDA", "github_about_section": "CUDA on non-NVIDIA GPUs", "homepage_link": "https://vosen.github.io/ZLUDA", "github_topic_closest_fit": "parallel-programming", "contributors_all": 15, "contributors_2025": 8, "contributors_2024": 4, "contributors_2023": 0, "growth_2025_percent": 100, "90-day-contributor-retention-rate": 0.25, "180-day-contributor-retention-rate": 0.2222222222222222 }, { "repo_name": "omnitrace", "repo_link": "https://github.com/ROCm/omnitrace", "category": "performance testing", "github_about_section": "Omnitrace: Application Profiling, Tracing, and Analysis", "homepage_link": "https://rocm.docs.amd.com/projects/omnitrace", "github_topic_closest_fit": "profiling", "contributors_all": 16, "contributors_2025": 2, "contributors_2024": 12, "contributors_2023": 2, "growth_2025_percent": -83, "90-day-contributor-retention-rate": 0.25, "180-day-contributor-retention-rate": 0.1875 }, { "repo_name": "BitBLAS", "repo_link": "https://github.com/microsoft/BitBLAS", "category": "Basic Linear Algebra Subprograms (BLAS)", "github_about_section": "BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment.", "github_topic_closest_fit": "matrix-multiplication", "contributors_all": 17, "contributors_2025": 5, "contributors_2024": 14, "contributors_2023": 0, "growth_2025_percent": -64, "90-day-contributor-retention-rate": 0.23529411764705882, "180-day-contributor-retention-rate": 0.125 }, { "repo_name": "flashinfer", "repo_link": "https://github.com/flashinfer-ai/flashinfer", "category": "gpu kernels", "github_about_section": "FlashInfer: Kernel Library for LLM Serving", "homepage_link": "https://flashinfer.ai", "github_topic_closest_fit": "attention", "contributors_all": 205, "contributors_2025": 158, "contributors_2024": 50, "contributors_2023": 11, "growth_2025_percent": 216, "90-day-contributor-retention-rate": 0.2265625, "180-day-contributor-retention-rate": 0.15217391304347827 }, { "repo_name": "nvcc4jupyter", "repo_link": "https://github.com/andreinechaev/nvcc4jupyter", "github_about_section": "A plugin for Jupyter Notebook to run CUDA C/C++ code", "homepage_link": "https://nvcc4jupyter.readthedocs.io", "contributors_all": 9, "contributors_2025": 0, "contributors_2024": 3, "contributors_2023": 3, "growth_2025_percent": -100, "90-day-contributor-retention-rate": 0.2222222222222222, "180-day-contributor-retention-rate": 0.1111111111111111 }, { "repo_name": "numpy", "repo_link": "https://github.com/numpy/numpy", "category": "scientific computing", "github_about_section": "The fundamental package for scientific computing with Python.", "homepage_link": "https://numpy.org", "github_topic_closest_fit": "scientific-computing", "contributors_all": 2172, "contributors_2025": 235, "contributors_2024": 233, "contributors_2023": 252, "growth_2025_percent": 0, "90-day-contributor-retention-rate": 0.21983786361468766, "180-day-contributor-retention-rate": 0.18807561803199224 }, { "repo_name": "SWE-bench", "repo_link": "https://github.com/SWE-bench/SWE-bench", "category": "benchmark", "github_about_section": "SWE-bench: Can Language Models Resolve Real-world Github Issues?", "homepage_link": "https://swebench.com", "github_topic_closest_fit": "benchmark", "contributors_all": 66, "contributors_2025": 33, "contributors_2024": 37, "contributors_2023": 9, "growth_2025_percent": -10, "90-day-contributor-retention-rate": 0.21428571428571427, "180-day-contributor-retention-rate": 0.14583333333333334 }, { "repo_name": "burn", "repo_link": "https://github.com/tracel-ai/burn", "github_about_section": "Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.", "homepage_link": "https://burn.dev", "contributors_all": 237, "contributors_2025": 99, "contributors_2024": 104, "contributors_2023": 62, "growth_2025_percent": -4, "90-day-contributor-retention-rate": 0.21359223300970873, "180-day-contributor-retention-rate": 0.13043478260869565 }, { "repo_name": "llama.cpp", "repo_link": "https://github.com/ggml-org/llama.cpp", "category": "inference engine", "github_about_section": "LLM inference in C/C++", "homepage_link": "https://ggml.ai", "github_topic_closest_fit": "inference", "contributors_all": 1374, "contributors_2025": 535, "contributors_2024": 575, "contributors_2023": 461, "growth_2025_percent": -6, "90-day-contributor-retention-rate": 0.21308724832214765, "180-day-contributor-retention-rate": 0.16818181818181818 }, { "repo_name": "Vulkan-Hpp", "repo_link": "https://github.com/KhronosGroup/Vulkan-Hpp", "category": "graphics api", "github_about_section": "Open-Source Vulkan C++ API", "homepage_link": "https://vulkan.org", "github_topic_closest_fit": "vulkan", "contributors_all": 102, "contributors_2025": 21, "contributors_2024": 15, "contributors_2023": 15, "growth_2025_percent": 40, "90-day-contributor-retention-rate": 0.20833333333333334, "180-day-contributor-retention-rate": 0.18085106382978725 }, { "repo_name": "pybind11", "repo_link": "https://github.com/pybind/pybind11", "github_about_section": "Seamless operability between C++11 and Python", "homepage_link": "https://pybind11.readthedocs.io", "github_topic_closest_fit": "bindings", "contributors_all": 404, "contributors_2025": 43, "contributors_2024": 45, "contributors_2023": 42, "growth_2025_percent": -4, "90-day-contributor-retention-rate": 0.2071611253196931, "180-day-contributor-retention-rate": 0.18441558441558442 }, { "repo_name": "modelcontextprotocol", "repo_link": "https://github.com/modelcontextprotocol/modelcontextprotocol", "category": "mcp", "github_about_section": "Specification and documentation for the Model Context Protocol", "homepage_link": "https://modelcontextprotocol.io", "github_topic_closest_fit": "mcp", "contributors_all": 327, "contributors_2025": 298, "contributors_2024": 42, "contributors_2023": 0, "growth_2025_percent": 609, "90-day-contributor-retention-rate": 0.20512820512820512, "180-day-contributor-retention-rate": 0.12987012987012986 }, { "repo_name": "Wan2.2", "repo_link": "https://github.com/Wan-Video/Wan2.2", "category": "video generation", "github_about_section": "Wan: Open and Advanced Large-Scale Video Generative Models", "homepage_link": "https://wan.video", "github_topic_closest_fit": "diffusion-models", "contributors_all": 14, "contributors_2025": 14, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.2, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "ComfyUI", "repo_link": "https://github.com/comfyanonymous/ComfyUI", "category": "user interface", "github_about_section": "The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.", "homepage_link": "https://comfy.org", "github_topic_closest_fit": "stable-diffusion", "contributors_all": 278, "contributors_2025": 108, "contributors_2024": 119, "contributors_2023": 94, "growth_2025_percent": -9, "90-day-contributor-retention-rate": 0.19753086419753085, "180-day-contributor-retention-rate": 0.16216216216216217 }, { "repo_name": "ccache", "repo_link": "https://github.com/ccache/ccache", "github_about_section": "ccache - a fast compiler cache", "homepage_link": "https://ccache.dev", "contributors_all": 218, "contributors_2025": 20, "contributors_2024": 28, "contributors_2023": 22, "growth_2025_percent": -28, "90-day-contributor-retention-rate": 0.18396226415094338, "180-day-contributor-retention-rate": 0.15384615384615385 }, { "repo_name": "transformers", "repo_link": "https://github.com/huggingface/transformers", "github_about_section": "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.", "homepage_link": "https://huggingface.co/transformers", "contributors_all": 3582, "contributors_2025": 860, "contributors_2024": 769, "contributors_2023": 758, "growth_2025_percent": 11, "90-day-contributor-retention-rate": 0.1778975741239892, "180-day-contributor-retention-rate": 0.146606914212548 }, { "repo_name": "mistral-inference", "repo_link": "https://github.com/mistralai/mistral-inference", "category": "inference engine", "github_about_section": "Official inference library for Mistral models", "homepage_link": "https://mistral.ai", "github_topic_closest_fit": "inference", "contributors_all": 29, "contributors_2025": 2, "contributors_2024": 17, "contributors_2023": 14, "growth_2025_percent": -88, "90-day-contributor-retention-rate": 0.1724137931034483, "180-day-contributor-retention-rate": 0.13793103448275862 }, { "repo_name": "synthetic-data-kit", "repo_link": "https://github.com/meta-llama/synthetic-data-kit", "category": "synthetic data generation", "github_about_section": "Tool for generating high quality Synthetic datasets", "homepage_link": "https://pypi.org/project/synthetic-data-kit", "github_topic_closest_fit": "synthetic-dataset-generation", "contributors_all": 15, "contributors_2025": 15, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.16666666666666666, "180-day-contributor-retention-rate": 0.0 }, { "repo_name": "accelerate", "repo_link": "https://github.com/huggingface/accelerate", "github_about_section": "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.", "homepage_link": "https://huggingface.co/docs/accelerate", "contributors_all": 392, "contributors_2025": 97, "contributors_2024": 124, "contributors_2023": 149, "growth_2025_percent": -21, "90-day-contributor-retention-rate": 0.16442048517520216, "180-day-contributor-retention-rate": 0.14772727272727273 }, { "repo_name": "mcp-agent", "repo_link": "https://github.com/lastmile-ai/mcp-agent", "category": "mcp", "github_about_section": "Build effective agents using Model Context Protocol and simple workflow patterns", "github_topic_closest_fit": "mcp", "contributors_all": 63, "contributors_2025": 63, "contributors_2024": 1, "contributors_2023": 0, "growth_2025_percent": 6200, "90-day-contributor-retention-rate": 0.1509433962264151, "180-day-contributor-retention-rate": 0.15625 }, { "repo_name": "trl", "repo_link": "https://github.com/huggingface/trl", "github_about_section": "Train transformer language models with reinforcement learning.", "homepage_link": "http://hf.co/docs/trl", "contributors_all": 433, "contributors_2025": 189, "contributors_2024": 154, "contributors_2023": 122, "growth_2025_percent": 22, "90-day-contributor-retention-rate": 0.14705882352941177, "180-day-contributor-retention-rate": 0.11904761904761904 }, { "repo_name": "neuronx-distributed-inference", "repo_link": "https://github.com/aws-neuron/neuronx-distributed-inference", "contributors_all": 11, "contributors_2025": 9, "contributors_2024": 3, "contributors_2023": 0, "growth_2025_percent": 200, "90-day-contributor-retention-rate": 0.14285714285714285, "180-day-contributor-retention-rate": 0.2 }, { "repo_name": "peft", "repo_link": "https://github.com/huggingface/peft", "github_about_section": "PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.", "homepage_link": "https://huggingface.co/docs/peft", "github_topic_closest_fit": "lora", "contributors_all": 272, "contributors_2025": 69, "contributors_2024": 111, "contributors_2023": 115, "growth_2025_percent": -37, "90-day-contributor-retention-rate": 0.13877551020408163, "180-day-contributor-retention-rate": 0.09051724137931035 }, { "repo_name": "letta", "repo_link": "https://github.com/letta-ai/letta", "category": "agent", "github_about_section": "Letta is the platform for building stateful agents: open AI with advanced memory that can learn and self-improve over time.", "homepage_link": "https://docs.letta.com", "github_topic_closest_fit": "ai-agents", "contributors_all": 157, "contributors_2025": 56, "contributors_2024": 75, "contributors_2023": 47, "growth_2025_percent": -25, "90-day-contributor-retention-rate": 0.13793103448275862, "180-day-contributor-retention-rate": 0.0948905109489051 }, { "repo_name": "unsloth", "repo_link": "https://github.com/unslothai/unsloth", "category": "fine tuning", "github_about_section": "Fine-tuning & Reinforcement Learning for LLMs. Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM.", "homepage_link": "https://docs.unsloth.ai", "github_topic_closest_fit": "fine-tuning", "contributors_all": 127, "contributors_2025": 102, "contributors_2024": 27, "contributors_2023": 3, "growth_2025_percent": 277, "90-day-contributor-retention-rate": 0.1348314606741573, "180-day-contributor-retention-rate": 0.11864406779661017 }, { "repo_name": "openevolve", "repo_link": "https://github.com/codelion/openevolve", "github_about_section": "Open-source implementation of AlphaEvolve", "github_topic_closest_fit": "genetic-algorithm", "contributors_all": 46, "contributors_2025": 46, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.12, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "StreamDiffusion", "repo_link": "https://github.com/cumulo-autumn/StreamDiffusion", "category": "image generation", "github_about_section": "StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation", "homepage_link": "https://arxiv.org/abs/2312.12491", "github_topic_closest_fit": "diffusion-models", "contributors_all": 29, "contributors_2025": 0, "contributors_2024": 9, "contributors_2023": 25, "growth_2025_percent": -100, "90-day-contributor-retention-rate": 0.10344827586206896, "180-day-contributor-retention-rate": 0.06896551724137931 }, { "repo_name": "TileIR", "repo_link": "https://github.com/microsoft/TileIR", "category": "parallel computing dsl", "github_about_section": "TileIR (tile-ir) is a concise domain-specific IR designed to streamline the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). By employing a Pythonic syntax with an underlying compiler infrastructure on top of TVM, TileIR allows developers to focus on productivity without sacrificing the low-level optimizations necessary for state-of-the-art performance.", "github_topic_closest_fit": "parallel-programming", "contributors_all": 10, "contributors_2025": 10, "contributors_2024": 1, "contributors_2023": 0, "growth_2025_percent": 900, "90-day-contributor-retention-rate": 0.1, "180-day-contributor-retention-rate": 0.0 }, { "repo_name": "quack", "repo_link": "https://github.com/Dao-AILab/quack", "category": "kernel examples", "github_about_section": "A Quirky Assortment of CuTe Kernels", "contributors_all": 17, "contributors_2025": 17, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.09090909090909091, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "ollama", "repo_link": "https://github.com/ollama/ollama", "category": "inference engine", "github_about_section": "Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.", "homepage_link": "https://ollama.com", "github_topic_closest_fit": "inference", "contributors_all": 574, "contributors_2025": 202, "contributors_2024": 314, "contributors_2023": 97, "growth_2025_percent": -35, "90-day-contributor-retention-rate": 0.08414872798434442, "180-day-contributor-retention-rate": 0.06458333333333334 }, { "repo_name": "IMO2025", "repo_link": "https://github.com/harmonic-ai/IMO2025", "category": "formal mathematical reasoning", "github_about_section": "Harmonic's model Aristotle achieved gold medal performance, solving 5 problems. This repository contains the lean statement files and proofs for Problems 1-5.", "homepage_link": "https://harmonic.fun", "github_topic_closest_fit": "lean", "contributors_all": 2, "contributors_2025": 2, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.0, "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "torchdendrite", "repo_link": "https://github.com/sandialabs/torchdendrite", "category": "machine learning framework", "github_about_section": "Dendrites for PyTorch and SNNTorch neural networks", "contributors_all": 2, "contributors_2025": 1, "contributors_2024": 1, "contributors_2023": 0, "growth_2025_percent": 0, "90-day-contributor-retention-rate": 0.0, "180-day-contributor-retention-rate": 0.0 }, { "repo_name": "cupti", "repo_link": "https://github.com/cwpearson/cupti", "category": "performance testing", "github_about_section": "Profile how CUDA applications create and modify data in memory.", "github_topic_closest_fit": "profiling", "contributors_all": 1, "contributors_2025": 0, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": 0.0, "180-day-contributor-retention-rate": 0.0 }, { "repo_name": "kernels-community", "repo_link": "https://github.com/huggingface/kernels-community", "category": "gpu kernels", "homepage_link": "https://huggingface.co/kernels-community", "github_about_section": "Kernel sources for https://huggingface.co/kernels-community", "contributors_all": 9, "contributors_2025": 9, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": "not-enough-data", "180-day-contributor-retention-rate": "not-enough-data" }, { "repo_name": "GEAK-agent", "repo_link": "https://github.com/AMD-AGI/GEAK-agent", "category": "agent", "github_about_section": "It is an LLM-based AI agent, which can write correct and efficient gpu kernels automatically.", "github_topic_closest_fit": "ai-agents", "contributors_all": 9, "contributors_2025": 9, "contributors_2024": 0, "contributors_2023": 0, "growth_2025_percent": "No 2024 data", "90-day-contributor-retention-rate": "not-enough-data", "180-day-contributor-retention-rate": "not-enough-data" } ]