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AI factories are the new industrial engines — and their profitability hinges on how efficiently they generate intelligence.
The rise of AI reasoning models — capable of multi-step logic, complex decision-making, and real-time responsiveness — is redefining what’s possible with AI. But it also comes with a cost: these models demand significantly more compute during inference than their predecessors. That means the infrastructure powering them must become radically more efficient.
In this video, we break down the critical balance between performance, power, and profitability in modern AI inference. As reasoning models generate more valuable tokens and power more intelligent services, AI factories must maximize what they can produce within fixed power budgets. These factories are power-constrained by design — so performance per watt isn’t just a benchmark. It’s the foundation of profitability.
But achieving optimal performance per watt isn’t only about chip efficiency. It’s also about how intelligently inference is deployed. Balancing maximum throughput and minimum latency are the critical levers for growing gross profit from the same capital equipment — especially as reasoning models push computational demands to new heights.
If you’re leading infrastructure strategy, scaling AI services, or investing in the next wave of intelligent production, this is your roadmap. Watch how smarter inference and full-stack infrastructure drive the economics of tomorrow’s AI factories.
Learn more: https://www.nvidia.com/en-us/solutions/ai/inference/?ncid=so-yout-696353 and https://blogs.nvidia.com/blog/ai-factory-inference-optimization/?ncid=so-yout-777041
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