Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels.

Generating Efficient AI-Centric Kernels

Production-Grade Autoresearch. Ideal for GPU kernels, ML model development, feature engineering, prompt engineering, and other optimizable code.

Open source skill library for AI coding agents to write, optimize, and debug high performance compute kernels across CUDA, Triton, and quantized workloads.

Custom Linux kernels purpose-built for Apple Mac hardware

Repo containing artifacts for Neurips 2025 tutorial- How to Build Agents to Generate Kernels for Faster LLMs (and Other Models!)

Fastest MoE/LLM inference runtime for consumer and edge Blackwell GPUs. SN74 on Gittensor.

AMD CDNA/RDNA (MI300 gfx942 / MI350 gfx950 / RDNA4 gfx1201) GPU kernel optimization knowledge base, packaged as a Claude Code skill. 7,400+ merged-PR references + 53 ISA-grounded synthesis pages. Inspired by MIT Han Lab's KernelWiki.

Learn Triton by building FlashAttention from scratch — V2 kernels, persistent threads, mask DSL, profiling toolkit, bilingual docs

Extended TileLang as a unified DSL to enable high-performance kernel development for Near-Memory Computing, Distributed Memory AI Accelerators, and Networked Accelerators.

Automatic Triton kernel generation and optimization for Intel GPU, powered by Claude Code.

Noeris — autonomous kernel fusion discovery + Triton autotuning for LLM kernels and Gemma layer deeper fusion (A100/H100 wins).

METAL-SCI: a scientific compute benchmark for evolutionary LLM kernel search on Apple Silicon Metal

A collection of high-performance CUDA kernels and experiments for learning and optimizing GPU compute primitives.

Optimized Ubuntu Touch for Lenovo Tab M8 HD (TB-8505F) - Kernel improvements, performance tuning, boot experience, and system optimizations for the MediaTek Helio A22 tablet

Universal Windows Kernel & Hardware Optimization Suite (TR/EN)

⚡ Low-Latency AI Windows Kernel Optimizer for Gamers & Power Users! Eliminate micro-stutters, stabilize frame-times, optimize network/ping, cut input lag, and double battery life. Unleash RAM/CPU & boost workstation rendering via low-level tweaks. 100% FREE Open-Source engine with no bloatware—unleash pro hardware tuning & responsiveness! 🚀

Hand-tuned NVIDIA SASS kernels for RTX 3070 Ti (GA104, sm_86): 31,910 GFLOPS HGEMM, 41,721 dense-equiv 2:4 sparse, 11,453 GFLOPS Flash Attention, no cuBLAS / cuDNN / PyTorch. Includes cuasmR, a CRAN-ready R package for cubin read/write + GPU benchmark measurement. 6-chapter tutorial + Chladni-pattern memory layout study.

Learning CUDA GEMM optimization and profiling for AI infrastructure.

CUDA kernel optimization lab: GEMM, FlashAttention, quantization, and GPU performance learning.