AI Infrastructure: From Compute to Operations
AI workloads place specific demands on the infrastructure layer. This article outlines the engineering considerations behind running AI workloads reliably across public cloud, partner private cloud, and customer private cloud environments.
Compute and GPU Infrastructure
AI inference and training workloads are compute-intensive. GPU infrastructure must be provisioned, scheduled, and operated with attention to:
- GPU allocation and lifecycle management
- Compute density and thermal management
- Driver and firmware versioning
- Workload isolation and multi-tenancy
Networking
AI workloads generate significant east-west traffic. Network design must account for:
- High-bandwidth interconnects between compute nodes
- Low-latency paths for distributed inference
- Secure segmentation between environments
- Observability of network health
Storage
Model weights, datasets, and checkpoints require a storage layer that balances:
- Capacity and throughput
- Latency for hot data
- Durability and backup
- Cost across tiers
Security
AI infrastructure introduces new security considerations:
- Model and data protection
- Access control across environments
- Compliance with data residency requirements
- Supply chain integrity for images and dependencies
Orchestration and Automation
Running AI workloads at scale requires orchestration and automation:
- Workload scheduling and placement
- Auto-scaling of compute
- Infrastructure-as-code for reproducibility
- Automated patching and maintenance
Observability and Operations
Operations teams need visibility into the full stack:
- Compute, network, and storage metrics
- Workload performance and cost
- Incident detection and response
- Capacity planning
Conclusion
AI infrastructure is infrastructure first. The engineering discipline required to operate it — compute, networking, storage, security, orchestration, and observability — is the same discipline that underpins reliable cloud and data centre operations.
This article reflects ViewRich's engineering perspective on AI infrastructure. It is not a statement of commercial product availability.