ThunderKittens: Simple, Fast, and Adorable AI Kernels

TL;DR

ThunderKittens introduces a simplified framework with key abstractions for writing high-performance AI GPU kernels, achieving performance comparable or superior to existing solutions across various AI operations.

Contribution

The paper presents ThunderKittens, a framework that uses a small set of abstractions to simplify and accelerate the development of performant AI GPU kernels.

Findings

Matches or outperforms prior kernels for AI operations

Achieves 10-40% better performance on attention backwards

Outperforms baselines by up to 14x on linear attention

Abstract

The challenge of mapping AI architectures to GPU hardware is creating a critical bottleneck in AI progress. Despite substantial efforts, hand-written custom kernels fail to meet their theoretical performance thresholds, even on well-established operations like linear attention. The diverse hardware capabilities of GPUs might suggest that we need a wide variety of techniques to achieve high performance. However, our work explores whether a small number of key abstractions can drastically simplify the process. We present ThunderKittens (TK), a framework for writing performant AI kernels while remaining easy to use and maintain. Our abstractions map to the three levels of the GPU hierarchy: (1) at the warp-level, we provide 16x16 matrix tiles as basic data structures and PyTorch-like parallel compute operations over tiles, (2) at the thread-block level, we provide a template for overlapping asynchronous operations across parallel warps, and (3) at the grid-level, we provide support to help hide the block launch and tear-down, and memory costs. We show the value of TK by providing kernels that match or outperform prior kernels for a range of AI operations. We match CuBLAS and FlashAttention-3 on GEMM and attention inference performance and outperform the strongest baselines by $10-40\%$ on attention backwards, $8\times$ on state space models, and $14\times$ on linear attention.