Resources
Practical guidance for AI infrastructure.
Short guides to help structure conversations about GPU capacity, fabric and day-two operations.
Guides
GPU capacity planning checklist
What to define before you request accelerator capacity.
Read the guideHigh-speed fabric fundamentals
Questions to settle for distributed workloads and interconnect planning.
Read the guideManaged operations readiness
A short framework for discussing monitoring, incidents and service governance.
Read the guide
GPU capacity planning checklist
Before requesting accelerator capacity, write down what is fixed and what is still open. A useful request describes the workload before it names a GPU.
- The workload: training, inference, fine-tuning or HPC
- Required throughput and memory
- Preferred region
- Estimated volume, in GPUs or nodes
- Expected duration
- Data dependencies
- Who will operate the environment
High-speed fabric fundamentals
Distributed AI workloads create dependencies between compute nodes, the network fabric and storage. Settle these questions early:
- How nodes communicate during training or inference
- Sensitivity to latency and bandwidth
- Topology and failure domains
- How the fabric is managed
- How fabric health is observed
Managed operations readiness
Before operations are handed over, agree the following in writing, and record any response commitments explicitly in the contract.
- The assets covered
- Event monitoring
- Severity definitions
- Incident coordination
- Escalation ownership
- Change-management controls
- Reporting
- Support hours