Design
Heterogeneous Cluster Designer
Compose a cluster from multiple GPU pools — mix types (H100 + L40S) or vendors (NVIDIA + AMD). Each pool is a labelled, tainted node group; workloads are steered to the right GPUs. The designer aggregates capacity, power, and cost across pools.
1NVIDIA
GPU
Role
Nodes · 4
GPUs/node · 8
2NVIDIA
GPU
Role
Nodes · 4
GPUs/node · 8
Cluster totals · 2 pools
Total GPUs
64
Aggregate FP16
43.3 PF
GPU memory
4,096 GB
Racks
4
~15 kW/rack
Facility power
56 kW
Est. CapEx
$1.25M
GPUs only
Est. energy/mo
$5K
Vendors
NVIDIA
Heterogeneous scheduling
- Label and taint each pool by GPU type; target workloads with nodeSelector/affinity + matching tolerations.
- Keep a single distributed-training job on one homogeneous pool — collectives (all-reduce) need matched GPUs.
- Same-vendor mix: one GPU Operator manages all nodes; differentiate pools with the gpu.product node label.
Topology
Rendering diagram…
Bill of materialsEstimate
| Pool 1 · H100 SXM5 GPU | 32 | 8/node × 4 · training |
| Pool 2 · L40S GPU | 32 | 8/node × 4 · inference |
| InfiniBand (RDMA) | shared | RDMA |
| Parallel file system | 1 tier | shared |
| Rack | 4 | ~15 kW usable/rack |
| Kubernetes control plane | 3 | HA, shared across pools |
Planning estimates from illustrative pricing — not quotes.