How do you design clustering systems for heterogeneous compute environments (CPU, GPU, distributed clusters)?

Heterogeneous clustering systems distribute workloads based on compute type and optimize algorithm selection per hardware.

Modern ML platforms run on mixed infrastructure: CPUs for batch processing, GPUs for embedding-heavy clustering, and distributed clusters for large-scale computation. Workload schedulers assign clustering tasks based on resource profiles. GPU acceleration is used for distance computations, while CPU handles orchestration. This improves efficiency and reduces cost.