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Efficient GPU utilization is critical for large-scale machine learning in MLOps. By distributing workloads across multiple GPUs, organizations can reduce energy usage and operational costs while improving performance. Key strategies include optimizing multi-GPU communication, leveraging Kubernetes for scalability, and tuning performance bottlenecks through GPU sharing, NUMA-aware scheduling, and RDMA for data transfers. Proper orchestration can enhance efficiency, reduce costs, and expedite training times on massive datasets.

Distributed Training in MLOps: How to Efficiently Use GPUs for Distributed Machine Learning in MLOps

Enabling Multi-GPU Communication for Distributed Training

GPU — Accelerated Distributed Training on Kubernetes