Big tech is hungry for AI hardware. Its appetite is growing at an extraordinary rate for GPUs which are now the most expensive, and most coveted pieces of technology on the market.

To understand the scale at which AI infrastructure is expanding, you only need to look at Elon Musk’ s xAI. According to a recent press release, xAI has acquired a 1 million-square-foot piece of land in Southwest Memphis to increase its AI data center footprint – in addition to its primary Memphis site and a second new data center in Atlanta.

As part of this remarkable expansion, xAI plans to increase the number of NVIDIA GPUs it owns in 2025 to 1 million – up from 100,000 last year. Meta, OpenAI, and Microsoft( to name a few) are also on hardware spending sprees. that won’ t break the bank. In response, traditional cloud options – Cloud GPU and GPU-as-a-service( GPUaaS) – as well as bare metal cloud are fastemerging services, providing scalable, highperformance computing without delayed access when supply is tight.

Essentially, these services allow users to access and deploy GPUs in the cloud rather than purchasing and maintaining them on-site. Providers have strong relationships with vendors that can open access to cutting-edge hardware for customers of all sizes at fairer market rates. For instance, on-demand access to NVIDIA’ s incoming B200 will cost as little as $ 2.40 per hour via GPUaaS.

There are four main benefits of using cloud GPU or GPUaaS:

The AI goldrush is great for business and consumers. But there is a problem; demand for enterprise-grade GPUs is outstripping supply. Only last month, OpenAI’ s Sam Altman took to X to complain his company is“ out of GPUs” which slowed down the rollout of ChatGPT 4.5.

What’ s more, smaller tech companies and AI-focused startups are finding themselves at the back of the lunch line, eagerly waiting for access to the latest hardware – or paying above the odds to get it earlier. In a game with first mover advantage, you can appreciate the unfairness of the current landscape.

Choosing the right deployment model – virtualized or bare metal cloud?

With AI models growing exponentially in size, developers need powerful computing solutions

1. You get scalable performance on demand, overcoming the issue of unpredictable AI workloads. This is a more dynamic solution, aligning computing power with immediate needs, avoiding waste, and delivering cost-efficiency.

2. It breaks down the financial barriers in accessing advanced hardware. Purchasing an NVIDIA H200 can cost upwards of $ 25,000 per unit, but on a pay-as-you-go basis, they can be rented for a low as $ 2.49 per hour. This model allows companies to focus their capital on testing, improving and growing, instead of being locked into sizeable hardware investments.

3. It supports faster time to market. AI is advancing every day and delays can result in competitors gaining ground or getting ahead. By accessing the latest technologies, development cycles are accelerated, and project timelines can be cut down.

Intelligent CIO North America Issue 57 | Page 33

Big tech is hungry for AI hardware. Its appetite is growing at an extraordinary rate for GPUs which are now the most expensive, and most coveted pieces of technology on the market.