Daily Trending of Cuda in GitHub Fri, 15 May 2026 01:37:12 GMT http://mshibanami.github.io/GitHubTrendingRSS https://github.com/karpathy/llm.c <p>LLM training in simple, raw C/CUDA</p><hr> https://github.com/NVIDIA/nccl-tests <p>NCCL Tests</p><hr> https://github.com/deepseek-ai/DeepEP <p>DeepEP: an efficient expert-parallel communication library</p><hr> https://github.com/NVIDIA/cuopt <p>GPU accelerated decision optimization</p><hr> https://github.com/thu-ml/SageAttention <p>[ICLR2025, ICML2025, NeurIPS2025 Spotlight] Quantized Attention achieves speedup of 2-5x compared to FlashAttention, without losing end-to-end metrics across language, image, and video models.</p><hr> https://github.com/deepseek-ai/DeepGEMM <p>DeepGEMM: clean and efficient FP8 GEMM kernels with fine-grained scaling</p><hr> https://github.com/alibaba/rtp-llm <p>RTP-LLM: Alibaba's high-performance LLM inference engine for diverse applications.</p><hr> https://github.com/NVlabs/instant-ngp <p>Instant neural graphics primitives: lightning fast NeRF and more</p><hr> https://github.com/siboehm/SGEMM_CUDA <p>Fast CUDA matrix multiplication from scratch</p><hr> https://github.com/Dao-AILab/causal-conv1d <p>Causal depthwise conv1d in CUDA, with a PyTorch interface</p><hr> https://github.com/NVIDIA/cub <p>[ARCHIVED] Cooperative primitives for CUDA C++. See https://github.com/NVIDIA/cccl</p><hr> https://github.com/NVIDIA/cuCollections <p style="color:#586069;"><em>No description/README provided.</em></p> https://github.com/NVIDIA/nvbench <p>CUDA Kernel Benchmarking Library</p><hr> https://github.com/brucefan1983/GPUMD <p>Graphics Processing Units Molecular Dynamics</p><hr> https://github.com/mirage-project/mirage <p>Mirage Persistent Kernel: Compiling LLMs into a MegaKernel</p><hr> https://github.com/NVIDIA/CUDALibrarySamples <p>CUDA Library Samples</p><hr>