As AI continues to take over our imagination and industries, another trend has been quietly growing behind the scenes.

Graphics Processing Units (GPUs) are a type of advanced processing chip that, until recently, was heavily leveraged by the gaming industry and other highly visual use cases, originally designed to accelerate computer image processing.

However, the recent boom in generative AI and the reliance on LLMs means that GPUs are now becoming more common here. As leaders, CTOs and data specialists turn to these powerful chips to boost their big data projects, 2024 is the year that GPUs will further fuel the AI revolution.

If there is any question about GPU’s new dominance, it can be found in the numbers. The global market for GPUs is already valued at a huge $65.3 billion, projected to increase nearly five-fold by the end of the decade.

Meanwhile, Nvidia, a name already synonymous with the AI revolution, has been generating a large part of its recent revenue boom thanks to AI-accelerating chips like the H200 Tensor Core GPU that are particularly popular with CTOs.

Yet why are GPUs now the tour-de-force for AI projects? What has fueled the migration away from image processing to AI accelerators?

Let’s take a look at three of the major advantages of GPUs for AI projects.

Getting skyrocketing costs under control

For AI to be successful it must also deliver well against cost-performance metrics. However, many CIOs have found in recent years that simply adding more CPUs to increase the abilities of their big data models has caused expenditure to skyrocket.

As a result, AI projects are either being shelved due to out-of-proportion costs vs the business results delivered, or projects are being compromised by reducing the number of queries or scope of the analysis. These approaches will only undermine the future of AI for companies by failing to deliver the positive business outcomes and operations insights that made leaders take notice of the technology in the first place.

The scale of this trend was put into sharp focus by a new 2024 State of Big Data Analytics Report published this week by SQream, a data acceleration specialist startup from New York. Based on a survey with 300 data experts at enterprise-level organizations across the US, the report noted that 71% regularly experience ‘bill-shock’ associated with their data projects.

Yet despite the fact that all survey respondents had a budget of over $5BN, 98% still experience AI project failure with poor ‘cost-performance’ as a major contributor to these failures.

The report also pointed towards a GPU-driven future for enterprise data architectures, with three in four execs noting that they are looking to GPUs in the future to realize more impact and value from their projects.

By using GPUs, CTOs are able to solve the cost-performance challenge that’s rampant across AI initiatives by upping the computing ability of the infrastructure using GPUs. From the time needed to process huge data sets to the number of queries being run, these powerful chips can accelerate the pace of work, overcome logistical hurdles and reach results more quickly resulting in major savings for companies.

Creating new compute possibilities

The rise of GPUs across the AI industry also means a decline in the reliance of CPUs, the previous industry standard for data processing tasks. CPUs were designed for sequential processing, which means that tasks and queries will be actioned in the exact order they were queued in.

This approach is well-equipped to handle small data tasks quickly, meaning they have sufficed for many data projects of the past decade. However, in 2024 AI projects need to handle huge volumes of complex, often unstructured, data sets handling detailed queries in a non-linear fashion.

CPUs fall short when it comes to such complex workloads, which is why many experts have turned to GPUs instead. While saving costs to ensure AI projects can be maintained is a major advantage, GPUs are also creating entirely new possibilities thanks to their advanced capabilities.

These chips have thousands of cores optimized for parallel processing, which gives them the ability to process vast amounts of data simultaneously. Further, they can scale up to supercomputing levels, and they can draw from a broad and deep software stack.

This makes them perfect partners for enterprise projects and collaborative industry partnerships that come with more complex data infrastructure or fueling other use cases like virtual reality suites or digital twins that boost AI projects even further.

Shrinking AI’s carbon footprint

Finally, the environmental cost of AI is becoming increasingly clear. Although algorithms aren’t an obvious source of carbon emissions in the way that a diesel engine spewing fumes would be, each query comes with an associated carbon cost. For example, training Chat-GPT 3 and its LLM created 500 tons of carbon dioxide.

In addition, according to McKinsey more than 90% of S&P 500 companies and 70% of Russell 1000 companies now publish Environmental, Sustainability and Governance (ESG) reports in some form. Organizations that have plowed ahead with AI adoption will need to ensure that its associated carbon cost is declared in these reports.

Indeed, in a 2024 report from Deloitte titled The State of Digital Adoption, there is a growing demand for sustainable and resilient tech infrastructure. For this reason, it’s important to note that by incorporating GPUs into their wider tech stack, leaders can reduce their AI carbon footprint immediately.

Relying on an outdated tech stack means that companies will inherently be using more power and energy to maintain them. Much like upgrading an old diesel car for a sleek new hybrid is an environmentally friendly switch, the overall efficiency gains that come with changing from CPUs to GPUs equates to lower environmental impact.

A future of strategic GPU application

The case for adopting GPUs to support AI use cases is growing stronger by the day. However, as their myriad advantages become common knowledge, demand will grow in turn.

CIOs need to ensure they move quickly and strategically deploy GPUs, partnering with data experts where needed to leverage other complementary strategies that accelerate the efficiency of their data infrastructure.