The infrastructure behind AI at scale. Analysis of GPU performance, compute economics, global AI infrastructure, and enterprise GPU strategy, alongside the latest developments from Axe Compute.
Voice AI runs inside a latency budget of a few hundred milliseconds. That budget decides almost every infrastructure choice a team makes. Running voice at…
Read the article →Inference spending passed training spending in 2025, and the vendor forecasts published since have widened that gap rather than closed it. Enterprises have…
Read the article →Frontier AI compute sits inside a handful of American data centers. China’s fast-growing domestic build and Europe’s publicly funded push compete for what is…
Read the article →Volume production has not shortened the wait. Enterprise buyers ordering Blackwell Ultra capacity today still face lead times measured in the better part of a…
Read the article →Gartner predicts that 40% of generative AI solutions will be multimodal by 2027, up from just 1% in 2023. Multimodal means vision, audio, text, and video…
Read the article →The history of artificial intelligence is usually told as a story of ideas. It is better understood as a story of compute. Each major age of AI began when a…
Read the article →InfiniBand and Ethernet are the two network fabrics that connect GPUs in a training cluster. The choice between them sets how much of a cluster’s theoretical…
Read the article →AI hardware has changed the physics of the data center floor. A single rack of current AI accelerators now draws more power than a full row of traditional…
Read the article →The best NVIDIA GPU for AI is the one that matches the workload. A model serving millions of small inference requests has almost nothing in common, at the…
Read the article →Tell us what you need, and Axe Compute comes back with a concrete plan and a committed date.
