Modern CUDA Learn Notes with PyTorch for Beginners, 200+ CUDA Kernels, Tensor Cores, HGEMM, FA-2 MMA.
Kernl lets you run PyTorch transformer models several times faster on GPU with a single line of code, and is designed to be easily hackable.
Fast, differentiable sorting and ranking in PyTorch
row-major matmul optimization
Direct NVIDIA CUDA access for PHP. GPU-accelerated tensors, JIT-compiled kernels, and async execution—no Python required
A performance comparison of standard matrix functions between CPU and GPU using Nvidia CUDA on Visual Studio using C++
Rust KV-cache compression for LLM inference. Implements TurboQuant (Zandieh et al., ICLR 2026) plus PQO — our variant that drops QJL, adds a fused CUDA kernel, and shrinks the cache to ~20% of FP16 (49% total VRAM at 32K). mistral.rs integration.
A beginner's guide to CUDA programming
a custom CUDA kernel for windowed matrix multiplication
A collection of scripts on MLSys-related stuff
SNU CSE Scalable High Performance Computing (M1522.006700) - 2023 Autumn
Winning submission for StartHack 2024: HPC optimized multi-GPU/CPU inference
frontier.fast challenge repo for agents and submissions to push the frontier of AI!
An unofficial re-implementation of Cursor's warp decode MoE inference technique. Achieved nearly 2.0x speedup on MOE layer on Nvidia H20 at batch size 1.
True Gradient will guide you through it all; believing in it and continuing to move forward is what fosters growth. Don’t let the past control you—the noise within the past is the very source of your worries and suffering.
CUDA Concepts & Techniques: From skilled to expert
Custom PyTorch CUDA kernel implementing optimized ReLU activation with vectorization, performance profiling, and memory analysis on Tesla T4 GPU achieving 75% bandwidth efficiency.
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