NVIDIA / compute-evalLinks
Evaluating Large Language Models for CUDA Code Generation ComputeEval is a framework designed to generate and evaluate CUDA code from Large Language Models.
☆58Updated last month
Alternatives and similar repositories for compute-eval
Users that are interested in compute-eval are comparing it to the libraries listed below
Sorting:
- Perplexity GPU Kernels☆418Updated 3 weeks ago
- ☆227Updated this week
- TritonParse: A Compiler Tracer, Visualizer, and mini-Reproducer(WIP) for Triton Kernels☆139Updated this week
- AI Tensor Engine for ROCm☆243Updated this week
- kernels, of the mega variety☆466Updated 2 months ago
- Fast low-bit matmul kernels in Triton☆339Updated this week
- Applied AI experiments and examples for PyTorch☆289Updated 2 months ago
- Cataloging released Triton kernels.☆247Updated 6 months ago
- An experimental CPU backend for Triton☆139Updated 2 months ago
- A Quirky Assortment of CuTe Kernels☆388Updated this week
- A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.☆212Updated this week
- KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems☆505Updated last week
- ☆85Updated 9 months ago
- Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.☆199Updated this week
- extensible collectives library in triton☆88Updated 4 months ago
- Efficient implementation of DeepSeek Ops (Blockwise FP8 GEMM, MoE, and MLA) for AMD Instinct MI300X☆60Updated last week
- A Fusion Code Generator for NVIDIA GPUs (commonly known as "nvFuser")☆346Updated this week
- Official Problem Sets / Reference Kernels for the GPU MODE Leaderboard!☆69Updated 3 weeks ago
- Collection of kernels written in Triton language☆142Updated 4 months ago
- Fastest kernels written from scratch☆310Updated 4 months ago
- A curated collection of resources, tutorials, and best practices for learning and mastering NVIDIA CUTLASS☆205Updated 3 months ago
- ☆228Updated last year
- An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).☆260Updated 3 weeks ago
- 🚀 Collection of components for development, training, tuning, and inference of foundation models leveraging PyTorch native components.☆207Updated this week
- ☆41Updated this week
- ☆102Updated 7 months ago
- High-Performance SGEMM on CUDA devices☆98Updated 6 months ago
- Ahead of Time (AOT) Triton Math Library☆75Updated this week
- ☆110Updated 4 months ago
- CUDA Matrix Multiplication Optimization☆213Updated last year