Apple's MLX adding CUDA support (github.com)

Article summary

Apple's MLX is adding CUDA support, allowing for the use of NVIDIA hardware for machine learning tasks. The CUDA backend is currently in development and has been tested on Linux with CUDA 12 and SM 7.0 or higher. This addition aims to provide a unified memory architecture and improve performance. The work is sponsored by Apple.

Main themes

• CUDA support
• MLX development
• NVIDIA hardware
• Unified memory architecture
• Copyright and fair use
• Machine learning frameworks
• GPU compute
• API compatibility

What commenters say

• Implementing a compatible API does not necessarily infringe on copyright, as seen in the Google vs Oracle case.
• The addition of CUDA support to MLX may pose a threat to NVIDIA's dominance in the market, but it could also increase adoption of their hardware.
• The performance of MLX with CUDA support is currently limited by overhead, but optimizations are being made to improve it.
• Using a unified memory architecture with discrete GPUs may require a different approach due to memory stalls and copying.
• Reimplementing an API for compatibility does not necessarily mean copying or infringing on the original, and fair use may apply.
• The use of CUDA on non-NVIDIA hardware is not possible without significant modifications or workarounds.
• The development of MLX with CUDA support is seen as a positive step towards increasing compatibility and adoption of machine learning frameworks.
• The legal implications of implementing compatible APIs are complex and require careful consideration of copyright and fair use laws.