What happens when the growth of computing demand becomes so rapid that even the best systems become unable to match it? This became a pressing question in the year 2025, as the areas of quantum computing,

Artificial intelligence is colliding with a hard physical limit: the energy and heat of conventional chips. As models scale into the trillions of parameters, simply throwing more silicon and electricity at the problem is becoming untenable for data centers and edge devices alike.

Nvidia CEO Jensen Huang says AI model growth is driving soaring GPU demand as the firm advances next-gen Rubin and Vera chips.

The sprint to build AI is no longer just about chips and algorithms. It’s about infrastructure, and North Texas power players are leading the way.

HP's latest EliteBook X G2 Series redefines business computing for the AI age with unprecedented performance, modular options, premium build, and mobility.

AI workloads are breaking traditional storage, and without faster, more reliable data systems, even the most powerful GPUs sit idle.

The global data center sector is set to nearly double in size over the coming four years, scaling to deliver up to 200 gigawatts – mainly to serve burgeoning AI requirements, along with hyperscale cloud needs. Currently, the sector delivers 103 GWs of power. For perspective, just 1 GW of power is enough to power about 750,000 homes.

Power generation resource availability is the growing concern for AI and data computing firms which are concerned that the utility grid may not match the anticipated growth