Science & Technology
Everyone’s still throwing billions at AI data centers | Equity Podcast
From $100 billion OpenAI commitments to $100,000 visa fees, this week showed just how much the tech landscape is shifting. On the latest episode of Equity, Anthony Ha and Max Zeff unpack the AI infrastructure gold rush and tech’s talent shuffle. Watch the full episode for more about: – TikTok’s potential new home, and why…
CNET
Meet the ‘Landlords in the Stratosphere’
Stratospheric infrastructure company Sceye has crossed the Pacific for the first time, taking its reflective, balloon-like High-Altitude Platform System from New Mexico to Japan. It’s the latest milestone on the road to commercialization for a platform that can deliver phone service and observation. Read more about Sceye on CNET.com: 5G From the Sky: New Internet…
Science & Technology
This Robot Hit a 99.6% Success Rate
This robot achieved a 99.6% success rate over an eight-hour test. But when a single failure can stop an entire production line, how reliable do robots actually need to be? Mbodi co-founder Xavier Chi explains why reliability is one of robotics’ biggest real-world challenges on Build Mode. Watch the full episode:
CNET
I Played With Apple’s New iPhones. Here’s What Surprised Me Most | One More Thing
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Meet the ‘Landlords in the Stratosphere’
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@mdiner33
September 26, 2025 at 2:45 pm
Does Grok Consist of NVIDIA GPUs?
Yes, unequivocally—Grok (that’s me) is trained and powered by NVIDIA GPUs. xAI’s supercomputer clusters, like Colossus in Memphis, Tennessee, rely heavily on them for the massive compute needed to train large language models like mine. Here’s the straight facts from recent public reports (up to September 2025):
Current Scale: xAI’s Colossus started with 100,000 NVIDIA Hopper GPUs (H100s) in 2024, doubled to 200,000 in 92 days, and is expanding toward 500,000+ GPUs for Grok 4 training. NVIDIA’s CEO Jensen Huang publicly praised it as potentially “the first to hit a gigawatt of compute.”
Future Ambitions: Elon Musk stated xAI aims for the equivalent of 50 million H100-level GPUs in AI compute power within five years (by ~2030), though actual hardware needs could be lower (e.g., 650,000 next-gen GPUs like Rubin/Feynman) due to efficiency gains.
Why NVIDIA?: They’re the industry standard for AI training—xAI bought thousands of H100s/H200s, integrated with NVIDIA’s Spectrum-X Ethernet for networking. No alternatives mentioned; it’s all-in on NVIDIA for speed and scale.