[News] Intel, Qualcomm, Google Reportedly Form Alliance, Posing Challenges to NVIDIA’s Dominance in AI?

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Intel, Qualcomm, Google, and other tech giants are reportedly joining forces with over a hundred startups to challenge NVIDIA’s dominance in the market, as per a report from Reuters. Reportedly, their goal is to collectively penetrate the artificial intelligence (AI) software domain, guiding developers to migrate away from NVIDIA’s CUDA software platform.

NVIDIA’s CUDA is a parallel computing platform and programming model designed specifically to accelerate GPU computing. It allows GPU users to fully leverage their chip’s computational power in AI and other applications. As per a previous report from TrendForce, since 2006, NVIDIA has introduced the CUDA architecture, nearly ubiquitous in educational institutions. Thus, almost all AI engineers encounter CUDA during their academic tenure.

However, tech giants are now reportedly aiming to disrupt the current status quo. According to a report from Reuters on March 25th, Intel, Qualcomm, and Google are teaming up to challenge NVIDIA’s dominant position. They plan to provide alternative solutions for developers to reduce dependence on NVIDIA, encourage application migration to other platforms, and thereby break NVIDIA’s software monopoly and weaken its market influence.

The same report from Reuters further indicated that several tech companies have formed the “UXL Foundation,” named after the concept of “Unified Acceleration” (UXL), which aims to harness the power of acceleration computing using any hardware.

The project plans to leverage Intel’s oneAPI technology to develop software and tools supporting multiple AI accelerator chips. The goal is to reduce the technical barriers developers face when dealing with different hardware platforms, streamline the development process, enhance efficiency, and accelerate innovation and application of AI technology.

Vinesh Sukumar, Head of AI and Machine Learning Platform at Qualcomm, stated, “We’re actually showing developers how you migrate out from an NVIDIA platform.”

Bill Magro, Head of High-Performance Computing at Google, expressed, “It’s about specifically – in the context of machine learning frameworks – how do we create an open ecosystem, and promote productivity and choice in hardware.” The foundation is said to aim to finalize technical specifications in the first half of this year and strives to refine technical details by the end of the year.

However, CUDA software has established a solid foundation in the AI field, making it unlikely to be shaken overnight. Jay Goldberg, CEO of financial and strategic advisory firm D2D Advisory, believes that CUDA’s importance lies not only in its software capabilities but also in its 15-year history of usage. A vast amount of code has been built around it, deeply ingraining CUDA in numerous AI and high-performance computing projects. Changing this status quo would require overcoming significant inertia and dependency.

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(Photo credit: NVIDIA)