The AI Factory Blueprint: Designing for Scalable, Efficient Inference
As AI moves from pilot projects to full-lifecycle production, enterprises face a critical question: how do we operationalize AI without breaking performance or cost boundaries?
In this session, experts from WEKA and NVIDIA will explore how modern AI Factories must evolve to meet the demands of high-throughput inference, agentic AI workloads, and enterprise-scale deployment.
We’ll unpack the unified data pipeline architecture reshaping AI infrastructure, the trade-offs involved in overcoming memory and data bottlenecks, and the design patterns enabling real-world performance with production benchmarks and deployment examples.
Agenda
- See how AI infrastructure is evolving into integrated “factories” purpose-built for large-scale inference, with hybrid architectures that bridge edge, core, and cloud deployments.
- Learn how NVIDIA’s AI solutions and NeuralMesh™ by WEKA remove traditional data and throughput constraints, with real-world examples spanning large-scale inference, complex scientific workloads, and high-speed analytical applications.
- Discover how architectural decisions around memory, data paths and token warehousing influence TCO, and how a forward-thinking deployment strategy can future-proof your infrastructure investment, supercharging ROI and AI project viability.
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