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HazyResearch avatar

HazyResearch/ThunderKittens

0
View on GitHub↗
3,448 stars·296 forks·Cuda·MIT·1 view

ThunderKittens

Tile primitives for speedy kernels

Features

  • Computation and Optimization - Framework for writing fast deep learning kernels in CUDA.

Star history

Star history chart for hazyresearch/thunderkittensStar history chart for hazyresearch/thunderkittens

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Frequently asked questions

What does hazyresearch/thunderkittens do?

Tile primitives for speedy kernels

What are the main features of hazyresearch/thunderkittens?

The main features of hazyresearch/thunderkittens are: Computation and Optimization.

What are some open-source alternatives to hazyresearch/thunderkittens?

Open-source alternatives to hazyresearch/thunderkittens include: arogozhnikov/einops — Einops is a tensor manipulation library that provides a framework-agnostic interface for reshaping, Einstein… bitsandbytes-foundation/bitsandbytes — bitsandbytes is a deep learning quantization tool and library designed to reduce the memory footprint of large… cupy/cupy — CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and… dask/dask — Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows… deap/deap. adapter-hub/adapters — A Unified Library for Parameter-Efficient and Modular Transfer Learning.