Scaling AI is not really a model problem at all. It is an infrastructure problem wearing a model problem’s clothes.
Production AI gives you none of the slack of the lab. Training, inference, retrieval, fine-tuning, and increasingly agentic workflows all run at once, and behave nothing like the systems most organizations still lean on.
Watch now to hear NVIDIA and WEKA map the journey from traditional HPC toward AI Factory architecture: where the bottlenecks bite, what separates the leaders, and how compute, data, and orchestration come together as one system.
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What you’ll learn
- Why storage breaks first, and why an idle GPU is almost always a data problem in disguise.
- The four failure points that recur, almost always in the same order: storage, data pipelines, GPU utilization collapse, and orchestration.
- What an AI Factory really is: not a data center with more GPUs, but infrastructure designed around a single output—tokens.
- Why parallel compute, data, and orchestration must function as one system, and what happens when they are treated as independent procurement decisions.
- The new metrics that matter: time to first token, token throughput, and token-per-watt.
- What leaders do differently from laggards, from designing the data layer before scaling compute to planning for production from day one.
What you’ll leave with
By the end of this session, you will be able to place your own infrastructure on the map and determine your next move.
- A clear view of the architectural arc, from traditional HPC, to accelerated HPC, to AI-native infrastructure, to the AI Factory.
- The ability to spot the four failure points early, before they show up as costly, hard-to-diagnose underperformance.
- The metrics that matter: time to first token, token throughput, and token-per-watt, rather than GPU uptime and job completion rates.
- The leaders’ playbook: design the data layer before scaling compute, start from a validated reference architecture, and plan for production from day one.
- A sharper case for your next investment, because GPU performance depends far more on data delivery and coordination than on raw compute.
Don’t just play catch-up.
The organizations furthest along built the factory before they needed to fill it. That is exactly what lets them move fast when an opportunity appears, while their peers are still negotiating storage procurement.
