This page may contain stale information. Last updated: 2026-04-24
Definition
ML infrastructure refers to the hardware, software, and networking components that support machine learning model training and deployment at scale.
Components
- Compute: GPU/TPU clusters for training
- Storage: Large-scale data storage for training data
- Networking: High-bandwidth networks for distributed training
- Orchestration: Frameworks for managing distributed workloads
- Monitoring: Systems for tracking training progress and failures
2026 Developments
DiLoCo Framework (Google DeepMind)
- Distributed training across thousands of chips
- 88% goodput despite hardware failures
- Bandwidth reduction enabling broader participation
Infrastructure Trends
- Training costs 100M+ for frontier models
- Increasing focus on efficiency and fault tolerance
- Cross-site training becoming viable for more organizations