Solving the Cold Start Problem in AI Inference

In this episode of Inference Time Tactics, Rob, Cooper, and Byron sit down with Prashanth Velidandi, co-founder of InferX, to explore how serverless inference is tackling the AI “cold start problem.” They dig into why 90% of the model lifecycle happens at inference—not training—and how cold starts and idle GPUs are crippling efficiency. Prashanth explains InferX’s snapshot technology, what it takes to deliver sub-second cold starts, and why inference infrastructure—not just models—will define the next era of AI. We talked about:   Why inference represents 90% of the model lifecycle, compared to the training focus most of the industry has. How cold starts and idle GPUs create massive inefficiencies in AI infrastructure. InferX’s snapshot technology that enables sub-second model loading and higher GPU utilization. The challenges of explaining and selling deeply technical infrastructure to the market. Why enterprises care about inference efficiency, cost, and reliability more than model size. How serverless inference abstracts away infrastructure complexity for developers. The coming explosion of multi-agent systems and billions of specialized models. Why sustainable innovation in AI will come from inference infrastructure. Connect with InferX Prashanth Velidandi https://inferx.net  https://x.com/pmv_inferx  https://www.linkedin.com/in/prashanth-velidandi-98629b115 Connect with Neurometric: Website: https://www.neurometric.ai/  Substack: https://neurometric.substack.com/  X: https://x.com/neurometric/  Bluesky: https://bsky.app/profile/neurometric.bsky.social   Rob May https://x.com/robmay  https://www.linkedin.com/in/robmay   Calvin Cooper https://x.com/cooper_nyc_  https://www.linkedin.com/in/coopernyc   Byron Galbraith https://x.com/bgalbraith  https://www.linkedin.com/in/byrongalbraith