Event details
Do you need more GPUs, or more from the ones you have?
Most AI organizations run AI training and inference workloads on separate pools of GPUs. Inference capacity is sized for peak daytime demand, which means those GPUs often sit idle overnight, right when researchers want to launch large training jobs.
The result is two expensive pools of infrastructure, each underused while the other is busy.
Join CoreWeave for a 40-minute deep dive into a different approach: running training, inference, eval, and research together on one cluster. You’ll learn how the SUNK Pod Scheduler places Kubernetes workloads on Slurm-managed nodes, helping teams increase GPU utilization without simply adding more capacity.
You’ll also see a demo of an inference deployment running alongside an active training job on a single SUNK cluster.
In this webinar, we’ll cover and demonstrate:
- How overnight training demand can put idle inference GPUs to work
- How SUNK Pod Scheduler places Kubernetes workloads on Slurm-managed nodes
- How to run training, inference, eval, and research together without separate GPU pools
- What co-locating an inference deployment with a running training job looks like in practice
See how to meet more demand without buying more GPUs.
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