Scaling AI at Inference: The Road to Agent-Driven ROI
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Roman Chernin joins Patrick Moorhead and Daniel Newman to discuss how AI infrastructure is shifting from training to inference, why Nebius built Token Factory to optimize system-level performance, and how agent-driven ROI will define AI success in 2026 and beyond.
AI has moved beyond model training, inference is the new frontier.
This Six Five Webcast features Patrick Moorhead and Daniel Newman, joined by Roman Chernin, Co-founder & Chief Business Officer at Nebius, to explore how AI infrastructure is evolving from massive training clusters to production-grade inference systems built for agents, open-source models, and real ROI.
Nebius positions itself as an AI-specialized cloud, purpose-built to optimize inference workloads at scale. As AI shifts from research labs to product companies and enterprise agents, performance, cost efficiency, and system-level orchestration have become the defining battleground.
Key Takeaways:
🔹 The shift from training to inference: Why budgets, architectures, and customer priorities are changing.
🔹 The Nebius Token Factory: How full-stack optimization across hardware, software, and orchestration improves unit economics.
🔹 Open-source in the enterprise: Why flexibility, tunability, and cost control matter as much as frontier intelligence.
🔹 Agent-driven ROI: Why 2026 will demand measurable business outcomes, not just model benchmarks.
🔹 Performance beyond GPUs: How CPUs, workload orchestration, caching, quantization, and stack optimization tie in to define success.
Nebius combines next-generation silicon access with a purpose-built cloud stack and white-glove technical support to help customers ship AI products that are fast, affordable, and compliant at scale.
The next phase of AI won’t be defined by a model, it will be defined by who can run inference most efficiently.
To learn more about how Nebius is scaling AI for real-world inference and agent-driven ROI, read about it here and explore the full solution: HERE
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