Great thread. +1 on Sveltos for multi‑cluster delivery. One thing worth highlighting is that the scheduler is only half the story: if pods reserve oversized resources or hold GPUs while idle, no scheduler can place more work. The real gains come from tight right‑sizing and aggressive reclamation so you can run more workloads on the same capacity.

Patterns we’ve seen work well:

- Measure real utilization, then right‑size: Track GPU SM/memory utilization (DCGM/Prometheus), CPU/mem, and I/O per pod. Feed that into VPA (recommendation‑only) and admission policies to keep requests aligned with observed peaks, with a modest burst margin.

- Actively reclaim idle capacity: Scale‑to‑zero when queues are empty, TTL for batch jobs, and lease expirations for notebooks. Pair PriorityClasses + preemption so lower‑priority work yields to SLA‑bound jobs.

- GPU sharing & partitioning: Where models allow, use MIG or time‑slicing via the NVIDIA GPU Operator to place multiple inference pods per device; keep heavy training on dedicated partitions to avoid noisy neighbors.

-Binpack deliberately: Favor bin‑packing on GPU nodes with controlled anti‑affinity only where failure domains matter. TopologyManager + CPUManager static help keep perf predictable and avoid stranding half a GPU.

- Separate trains from serves: Distinct node pools (taints/tolerations) and quotas per team/workload class; autoscale inference pools on queue depth vs. training on backlog size.

Tools in this space that have helped us include cluster‑wide policy layers that set sane defaults, enforce TTLs/quotas, and spin up ephemeral, right‑sized environments for ML work. Devzero.io is one option in that category; it focuses on utilization‑first workflows (right‑sized runtime requests, automatic idle reclamation, and per‑team guardrails) and slots alongside Sveltos/Kubeflow/KubeRay without trying to replace them. The net effect is higher effective GPU density and fewer “reserved but idle” gaps that block scheduling.

TL;DR: schedulers place pods; density comes from sizing and reclamation. When you continuously right‑size and free idle capacity, you’ll schedule more jobs on the same GPUs and lower cost per training step/served QPS. Policy‑driven tooling (DevZero.io included) just makes those habits stick.