GPU FinOps & NVIDIA Alternatives – Is the Risk Worth the Reward?
With GPU demand far outstripping supply, enterprises are looking beyond NVIDIA to control cost and risk: alternate accelerators, on-prem GPU clusters, and creative scheduling policies for training versus inference. This session interrogates the “NVIDIA + X” future: performance parity, ecosystem maturity, operational complexity, and what happens to your AI roadmap if you don’t diversify.
Key Questions: – How should enterprises frame vendor concentration risk around GPUs and AI accelerators? – What’s the real operational cost of adding a second accelerator stack (tooling, drivers, frameworks, talent)? – How do GPU FinOps practices change when you’re running private LLMs versus calling hosted APIs? – How can AI-aware schedulers and capacity planning keep throughput, latency, and cost aligned as AI usage explodes?
Takeaways: – A FinOps-oriented lens on NVIDIA versus alternative accelerators, with concrete risk/benefit views. – Practical design patterns for policy-based scheduling of training, fine-tuning, and inference workloads across heterogeneous GPU estates.
As the head of Citi’s Security Operations and Threat Management Technology team, I manage a team of cybersecurity engineers that enable and support Citi’s Cyber Security Operations with innovative technology services, advanced controls, and automated containment and remediation for advanced threats.
