The software that supports the hardware is just as important as the hardware itself The artificial intelligence revolution has created unprecedented demand for specialized computing hardware, but today’s AI systems …

AI infrastructure demands massive amounts of energy — is there anything we can do about it? The race to build more powerful AI models has sparked an energy crisis. While …

A new class of GPU providers have risen up in the era of AI The rise of AI has created an unprecedented hunger for GPU compute — and traditional cloud …

Nvidia’s investment in Synopsys continues the company’s march towards vertical integration — or at least vertical influence Nvidia has made no secret of its ambitions to extend its reach far …

Amazon is making waves with Trainium3, but already attention is moving to Trainium4 In sum – What we know: Amazon Web Services used its re:Invent 2025 conference to unveil a …

GPU-as-a-Service eliminates the need to own the hardware, but what are the trade-offs? The race to build and deploy AI has created an infrastructure bottleneck that’s reshaping how organizations think …

In sum – what to know: Meta explores a major TPU shift – Talks with Google could see Meta rent TPUs in 2026, signaling a meaningful move to diversify beyond …

In sum – what to know: Trainium3 marks a major generational jump – AWS’ new 3nm accelerator delivers over 4x gains in compute, and is designed to scale from 144-chip …

AI compute isn’t one thing. It’s two. Under the umbrella of “AI workloads,” training and inference represent distinct computational worlds with different goals, hardware profiles, and economics. They often get …

For decades, compute has scaled faster than memory. Processors can execute more operations every year, but the speed at which data moves in and out of memory has lagged behind. …