Ignite Startups: How Thunder Compute is Maximizing GPU Efficiency with Carl Peterson

The world of artificial intelligence is evolving at a breakneck pace, with demand for computing power growing exponentially. AI models require massive amounts of GPU processing, yet a significant portion of this compute power remains underutilized. Enter Thunder Compute, a YC-backed startup that is revolutionizing how GPUs are allocated and used, dramatically improving efficiency and cost-effectiveness for AI developers and enterprises.

In this episode of Ignite Podcast, host Brian Bell sat down with Carl Peterson, founder and CEO of Thunder Compute, to discuss how his company is tackling one of the biggest inefficiencies in AI infrastructure. Their conversation delved into GPU virtualization, the future of AI computing, and what it means for the industry.

The Problem: GPUs Sitting Idle 60–90% of the Time

At the heart of Thunder Compute’s innovation is a shocking inefficiency in AI computing: most GPUs sit idle for the majority of their lifecycle. While enterprises are paying a premium to access cloud-based GPUs, studies show that these processors are often underutilized, with as much as 60–90% of their time spent doing nothing.