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AI economics part 2
AI labs are hitting GPU supply limits where efficiency improvements matter more than adding raw compute capacity.
What: Thread from @sriramkri examining GPU resource constraints facing AI labs, arguing that scaling GPU count doesn't linearly scale compute performance due to interconnect and coordination overhead, making efficiency optimizations more valuable than raw hardware expansion given finite supply.
Why it matters: Signals a shift in AI development strategy from a scaling-focused race to efficiency-focused competition, as supply constraints force labs to optimize existing infrastructure rather than simply buying more GPUs.
Original article
AI labs are in an ongoing war over GPU resources. That article looks into demand and supply and how the infrastructure powering AI today may not be sufficient. Scaling GPUs doesn't scale compute linearly. Efficiency matters more at raw scale given finite supply.
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