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Giant Companies Turning to AI: How to Address GPU Shortage

Giant Companies Turning to AI: How to Address GPU Shortage
Paul L.

In recent years, the surge in AI development has led to groundbreaking advancements, transforming industries and shaping the future of technology. According to Statista, the AI market is expected to reach $305.90 billion in 2024 and shoot up to $738.80 billion by 2030. However, this rapid growth has sparked a significant challenge: a global GPU shortage.

Once used for gaming, Graphics Processing Units (GPUs) now play a pivotal role in training complex AI models due to their ability to perform parallel operations essential for machine learning tasks. AI research, especially in deep learning, requires vast amounts of data processing, which is now pushing the limits of available GPU resources.

AI’s Role in GPU Shortage 

At the heart of the AI boom is the need for immense GPU power. As AI development is spreading across almost all industries, the growing demand for GPUs is not just from tech giants like Apple that recently teased its upcoming AI initiative, but also from other fields, all vying for a piece of the computational pie to drive innovation. It is evident from the fact that over 50% of all ‘AI in chemistry’ documents have been published in the past four years, highlighting the rapid adoption of deep learning (DL) and the consequent surge in GPU utilization​​.

DL in Drug Discovery

The integration of DL in computational drug discovery has significantly democratized the field, making drug discovery processes more accessible to a broader scientific community. DL models, which predict docking outcomes or filter large chemical libraries, rely heavily on GPUs for their computational power​​. This surge in AI applications across drug discovery is one of the key elements that has led to an increased demand for GPUs, contributing to the global GPU shortage.

Apple’s AI Initiative

The involvement of major corporations in AI research exacerbates the GPU shortage. A notable instance is Apple’s announcement, as reported by CNBC​​, indicating a significant AI initiative set to be unveiled later this year. It is potentially signaling big companies’ investment in AI to stay competitive and innovative. Such announcements underline the escalating competition for GPUs, further straining the already limited GPU supplies.

AI’s Insatiable Energy Consumption

The situation is also illustrated in the energy and GPU consumption by AI technologies. Training AI models, especially those as complex as ChatGPT, requires a tremendous amount of energy, much of which is powered by GPUs. According to OpenAI, the company has already spent over $100 million in training the algorithm that powers ChatGPT. Sam Altman, the CEO of OpenAI, further stated that “the research strategy that birthed ChatGPT is played out and future strides in artificial intelligence will require new ideas”. This not only highlights the demand for GPUs but also raises concerns about the sustainability of AI advancements.

Another fact mentioned by Prof. Huaqiang Wu is that the energy efficiency of current neural network accelerators is significantly lower compared to the human brain’s efficiency. It emphasizes the need for innovation in hardware that can support AI’s growth without further straining resources​.

The Need for Alternatives

In response to this challenge, there is an innovative solution: leveraging the unused computing power of individuals, businesses and data centers and making them available for AI research and other GPU-intensive developments., as a decentralized cloud computing platform, takes this approach finding that the IT industry spends over $1 trillion on hardware every year, but 50% of this infrastructure is sitting idle or turned off.

By tapping into the vast, untapped reserve of idle computing resources, the platform enables AI researchers and developers to continue their work without the constraints imposed by the current GPU shortage. It remarkably reduces the pressure on GPU resources and promotes a more sustainable and cost-effective model for accessing computational power. stands out by providing a scalable and flexible alternative to traditional cloud services, which are often limited by the availability of hardware resources like GPUs.

About is a decentralized network of cloud computing aggregators. Through the platform, individuals and businesses, including AI startups of all sizes, gain access to cost-effective, easily scalable, and secure computing power. It introduces the world’s first decentralized mesh hyperscaler, SKYNET. The solution leverages the infrastructure of PRO to utilize unused computing resources from professional data centers by connecting them to a mesh network with the help of the distribution technology of GO.

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Paul L.