The team behind continuous batching says your idle GPUs should be running inference, not sitting dark
The team behind continuous batching says your idle GPUs should be running inference, not sitting dark Sean Michael Kerner March 12, 2026 Credit: Image generated by VentureBeat with Nano-Banana-2 Every GPU cluster has dead time. Training jobs finish, workloads shift and hardware sits dark while power and cooling costs keep running.
Voice: OpenAI TTS
Transcript
Izzo So here’s one that’s been making the rounds — The team behind continuous batching says your idle GPUs should be running inference, not sitting dark.
Izzo You’re listening to Exploring Next. I’m Izzo, and Boone’s here. Let’s get into it.
Boone Yeah, this caught my attention because training jobs finish, workloads shift and hardware sits dark while power and cooling costs keep running.
Izzo From a product standpoint, the interesting question is who actually ships with this. when the operator's scheduler needs hardware back, the inference workloads are preempted and GPUs are returned.
Boone Right, and technically friendliAI's engine is written in C++ and uses custom GPU kernels rather than Nvidia's cuDNN library.
Izzo Okay so what should people actually go try? The original source is a good starting point: https://venturebeat.com/infrastructure/the-team-behind-continuous-batching-says-your-idle-gpus-should-be-running
Boone Definitely read that first. And if you want to go deeper, look into related tools in the same space — build something small and see where it breaks.
Izzo Good call. That’s the episode — we’ll catch you on the next one.