Do you really need all those GPUs?

"For years, the narrative around artificial intelligence has centered on GPUs (graphics processing units) and their compute power. Companies have readily embraced the idea that expensive, state-of-the-art GPUs are essential for training and running AI models. Public cloud providers and hardware manufacturers have promoted this belief, marketing newer, more powerful chips as crucial for remaining competitive in the race for AI innovation."

"This decline may seem positive. Businesses save money, and cloud providers adjust supply. However, there's a critical shift in business decision-making behind these numbers. The price cuts did not result from an oversupply; they reflect changing priorities. Demand for top-tier GPUs is falling as enterprises question why they should pay for expensive GPUs when more affordable alternatives offer nearly identical results for most AI workloads."

Artificial intelligence has been widely associated with the need for cutting-edge GPUs and their compute capacity. Many enterprise AI workloads such as recommendation engines, predictive analytics, and chatbots do not require the latest high-end chips and can run effectively on older GPUs or commodity CPUs. Cloud-delivered high-demand GPU prices have fallen dramatically, with examples like AWS H100 Spot Instances dropping sharply. Those price declines reflect falling demand and shifting priorities rather than mere oversupply. As cost pressures increase, enterprises are reevaluating infrastructure choices, prioritizing pragmatic deployments that balance performance and cost by leveraging existing or lower-cost hardware.

Read at InfoWorld