Definition #
Physical and virtual components required to build, train, and deploy AI models at scale.
Key Characteristics #
- Accelerated computing (GPUs/TPUs)
- Distributed training frameworks
- Model serving architectures
- Monitoring/observability tools
Why It Matters #
Reduces model training time from weeks to hours (NVIDIA DGX benchmarks).
Common Use Cases #
- Large language model training
- Real-time inference systems
- Federated learning setups
Examples #
- NVIDIA DGX SuperPOD
- Kubeflow orchestration
- TensorFlow Serving
FAQs #
Q: On-prem vs cloud infrastructure?
A: Cloud offers elasticity, on-prem better for sensitive data - hybrid is common.
Q: Cost optimization strategies?
A: Use spot instances for training, edge devices for inference.