Apple Silicon machine learning code may become more easily portable to Nvidia hardware

A project is trying to cut the cost of making machine learning applications for Nvidia hardware, by developing on an Apple Silicon Mac and exporting it to CUDA.Nvidia's hardware is often used to process machine learning applicationsMachine learning is costly to enter, in part due to the expensive hardware it uses to service queries at speed. While Nvidia's chips and components are favored due to their performance, Apple is trying to make it easier for developers to use that hardware.Work is being carried out on Apple MLX, the company's open-source machine learning framework, to add CUDA backend support, spots Ycombinator. CUDA is Nvidia's software layer for working with its graphics processing units (GPUs) on graphics cards, which is also used to handle processing tasks such as machine learning. Continue Reading on AppleInsider | Discuss on our Forums