Event details
Infrastructure Built for Production-Scale AI
Today’s frontier and mixture-of-experts models weren’t small. They spanned multi-trillion parameters and required precise coordination across thousand-GPU clusters.
Traditional cloud environments simply weren’t built for this scale. To move from experimentation to real-world deployment, teams needed infrastructure purpose-built for sustained, large-scale workloads.
In this session, CoreWeave detailed how we optimized every layer of the AI stack—from infrastructure to orchestration to observability—to efficiently run large-scale training and inference workloads. We also examined the architectural breakthroughs that enabled rack-scale systems to operate with ultra-low latency and high reliability.
These were the essential cloud components that powered the next generation of agentic AI. The question was: How did your infrastructure stack up?
What you’ll learn in this on-demand session
- How infrastructure requirements change when scaling to trillion-parameter and mixture-of-experts models
- How full-stack optimization across infrastructure, orchestration, and observability improves performance and efficiency
- Architectural innovations enabling ultra-low latency, rack-scale AI systems
- Best practices for running production-grade AI workloads, including agentic AI systems
Speakers
