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wayne nelms
@wayne_nelmz
MIT, ex-SIG quant trader || building @ornnexchange 🇺🇸
加入 September 2025
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At this point, inference is turning AI capacity into a distributed power market. OpenAI and Anthropic are shopping for 20-30 MW sites because inference can be spread across locations that would never support a frontier training run. That raises the value of smaller powered sites and makes hardware redeployability more important than campus scale. > modular DCs > distributed grid > lower effective latency
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OPENAI & ANTHROPIC HUNT FOR 20-30 MW AI CAPACITY IN UK, NORDICS OpenAI and Anthropic are looking at smaller compute deployments across the U.K. and Nordics, with similar discussions also taking place in the U.S., per CNBC. Anthropic has explored 20-30 MW deals in the U.K. and Nordic region, while OpenAI has been looking at opportunities in the Nordics. The move would complement their much larger multi-hundred-MW and gigawatt projects by giving both labs faster access to powered capacity that can come online sooner. These smaller clusters are particularly useful for inference, where AI workloads can be spread across multiple sites instead of relying on one massive tightly connected training facility. That shift is becoming more important as more compute moves from training models to serving them in production. JLL expects inference to overtake training as a share of data center capacity in 2027 and reach 37% of global workloads by 2030. Anthropic already has a roughly $45B deal with Nscale for about 460 MW in West Virginia, while OpenAI has committed to several multi-GW Stargate projects across the U.S. Source: CNBC
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