AMD to acquire Open-Source AI software expert Nod.a

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AMD has announced the signing of an agreement to acquire to expand the company’s open AI software capabilities.

Seen as a bid to be able to compete more effectively with Nvidia, AMD said that it is planning to invest heavily in the critical software necessary for the company's advanced AI chips.

The addition of will bring an experienced team that has developed an industry-leading software technology that accelerates the deployment of AI solutions optimised for AMD Instinct data centre accelerators, Ryzen AI processors, EPYC processors, Versal SoCs and Radeon GPUs to AMD.

The agreement aligns with AMD’s AI growth strategy which is centred on an open software ecosystem that lowers the barriers of entry for customers through developer tools, libraries and models.

“The acquisition of is expected to significantly enhance our ability to provide AI customers with open software that allows them to easily deploy highly performant AI models tuned for AMD hardware,” said Vamsi Boppana, senior vice president, Artificial Intelligence Group at AMD. “The addition of the talented team accelerates our ability to advance open-source compiler technology and enable portable, high-performance AI solutions across the AMD product portfolio.’s technologies are already widely deployed in the cloud, at the edge and across a broad range of end point devices today.”

“At, we are a team of engineers focused on problem solving in an industry of constant change,” said Anush Elangovan, co-founder and CEO, “Our journey as a company has cemented our role as the primary maintainer and major contributor to some of the world's most important AI repositories, including SHARK, Torch-MLIR and OpenXLA/IREE code generation technology. By joining forces with AMD, we will bring this expertise to a broader range of customers on a global scale.” delivers optimised AI solutions to leading hyperscalers, enterprises and startups. The compiler-based automation software capabilities of’s SHARK software reduce the need for manual optimisation and the time required to deploy highly performant AI models to run across a broad portfolio of data centre, edge and client platforms powered by AMD CDNA, XDNA, RDNA and “Zen” architectures.