General Compute runs an inference cloud built on purpose-built AI accelerators rather than general-purpose GPUs, designed for the high volume of model calls that AI agents generate.
General Compute's architecture separates the prefill and decode stages of inference so each can scale independently, aiming for faster token throughput at lower power. The platform is built for developers and AI agents that need fast, high-volume inference, including agents that provision their own compute.
The constraint on AI capacity is buildings and cooling, not chips. Because the inference silicon is air-cooled, it drops into colocation space that already exists in weeks, while a liquid-cooled GPU build waits years for the site to be constructed.
NZVC invested in General Compute from NZVC Fund II. We file the company under Deep Tech on the portfolio wall.
We backed General Compute because AI agents are driving a step change in inference demand, and general-purpose GPUs are not the most efficient way to serve it. Betting on specialized accelerators and an architecture designed around agent workloads is a real technical position rather than a thin layer over existing clouds. This is an infrastructure market growing fast enough to reward a focused team that gets the engineering right, and the timing lines up with agents moving into production.

You hear a founder whose own hearing loss led him to build avatars that sign a children's book in real time. Then you follow AI teaching up the ladder, from a personal tutor built for one child to avatars standing in for Ivy League professors to a company that shipped 40 AI projects to production. You leave with one question: would you trust an AI to teach your child?
A joint episode, alongside Kara Technologies.
You hear how a high school dropout who went bankrupt in his twenties built a language platform with 35 million followers. Finn Puklowski explains why Brazil, why creators, and how AI changes what teaching looks like.