Insights on GPU infrastructure, AI, and data centers.
BIS shifted H200 exports to China from denial to case-by-case review while Congress advanced the AI OVERWATCH Act to ban Blackwell sales outright. The resulting policy collision reshapes AI infrastruc...
Verizon's deployment of NVIDIA GPUs at 1,000 edge locations, AT&T's $8 billion edge computing investment, and AWS Wavelength bringing cloud to 5G networks demonstrate the convergence of ultra-low
JPMorgan Chase's trading floor processes 3 billion market events daily through AI models that must complete inference in under 250 microseconds while simultaneously maintaining audit trails for every
Eight companies now compete to build data centers in orbit. From SpaceX's 1M-satellite filing to Google's TPU clusters and Starcloud's 88K constellation, we break down the players, economics, and time...
Detailed analysis of specialized GPU cloud providers beyond hyperscalers for cost-effective AI infrastructure.
Rack power densities have jumped from 8.2kW average in 2020 to projections of 500-1000kW for AI Factory deployments.¹ GPUs like NVIDIA's Blackwell B100 and B200 cross the 1,000-watt threshold per
Five hyperscalers will spend $660-690B on AI infrastructure in 2026. Microsoft alone sits on $80B in unfulfilled Azure orders because it cannot find enough electricity to power its GPUs.
The average AI rack will cost $3.9 million in 2025, compared to $500,000 for traditional server racks.¹ That sevenfold cost increase reflects the fundamental transformation in rack requirements as
Google TPU v7 rivals Blackwell. AWS Trainium3 hits 2.52 PFLOPS. Groq LPU delivers 750 tokens/sec. The AI accelerator landscape beyond NVIDIA's 80% market share.
Virginia's SB 253 would shift grid upgrade costs from residential ratepayers to data centers, raising their rates 15.8%. Other states are following suit.
DeepSeek-V3 demonstrates what Mixture of Experts architecture enables: a model with 671 billion total parameters that activates only 37 billion during inference, achieving GPT-4 level performance at
When OpenAI lost 72 hours of GPT-4 training progress due to a checkpoint corruption, the incident cost $8.6 million in wasted compute time and delayed product launch by two weeks. Disaster recovery
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