On Monday, Osaid announced the rurce software tools for artificial intelligence applications. These tools may make it simpler for developers to switch between various underlying chips.

According to Meta, its new open-source AI platform, PyTorch, can make code run up to 12 times faster on Nvidia Corp.'s NVDA.O flagship A100 chip or up to four times faster on Advanced Micro Devices Inc.'s AMD.O MI250 chip. PyTorch is an open-source machine learning framework.

However, the flexibility the software may offer is just as significant as the speed improvement, according to Meta.

Chipmakers are engaging in a major arms race to attract developers to utilise their chips, and software has emerged as a vital arena. For artificial intelligence development, Nvidia's CUDA platform has so far been the most well-liked.

But once programmers customise their code for Nvidia processors, it becomes challenging to run it on graphics processing units, or GPUs, from Nvidia rivals like AMD. According to Meta, the software is made to allow for simple chip swapping without being locked in.

According to a blog post by Meta, 'the unified GPU back-end support gives deep learning developers greater hardware vendor choices with minimum migration expenses.'

Requests for comments from Nvidia and AMD were not immediately responded to.

The AI task of 'inference' is where machine learning algorithms that have previously been trained on enormous amounts of data are used to make snap decisions, like determining whether a picture is of a cat or a dog.

'This software project is cross-platform. It also demonstrates the value of software, particularly when using neural networks for inference in machine learning 'said David Kanter, a founder of MLCommons, a non-profit organisation that gauges the speed of AI.

The new Meta AI platform, according to Kanter, would 'be beneficial for customer choice.'

Chipmakers are fighting to create an ecosystem of developers who will utilise their chips, and software has emerged as a critical front in the conflict. Up until now, the most widely used platform for artificial intelligence development has been Nvidia's CUDA.