Adopt
Why?
- Training large models and running high-throughput inference requires dense GPU racks.
- GPUs remain the dominant commodity hardware for deep learning, with strong software ecosystem (CUDA, cuDNN, PyTorch, TensorFlow).
- On-prem and hybrid deployment options are strategic for performance, cost, and data governance.
What?
- Standardize on dense GPU server designs (A100/H100 or equivalent), rack-level layout, and procurement strategies.
- Invest in GPU fleet management, node provisioning, and tenancy models (single-tenant vs multislot sharing).
- Evaluate cloud vs on-prem tradeoffs and build repeatable reference architectures for GPU farms.