The most expensive hardware in your cluster is also the most wasted. A pod asks for a whole GPU, uses 20% of it, and holds the rest hostage. Across a fleet, most teams strand 60 to 70% of their accelerator capacity.

Kubernetes treats a GPU as one indivisible unit by default. Three things change that:
- HAMi (CNCF) slices a physical GPU by memory and compute so many workloads share one card;
- Volcano adds gang scheduling and queue fairness so distributed jobs and teams coexist;
- and Dynamic Resource Allocation, GA since 2025, makes fine-grained, topology-aware assignment native to Kubernetes.

This session shows how to share and schedule accelerators in production: fractional GPUs, gang scheduling, fair queues, and how the same approach handles heterogeneous chips beyond NVIDIA. We'll walk real utilization numbers before and after.

You'll leave able to run more workloads on the GPUs you already pay for.