High CPU/GPU Requirements – Be aware that your VM has sufficient resources i.e. to avoid bottlenecks. CPU, GPU, and memory, to perform resource-intensive functions – AI workloads can demand dynamic scaling of resources, especially for cloud-based VMs
Network Latency – If large datasets stored in multiple locations, network latency can become a significant performance bottleneck.
Cost Management – Important to monitor & manage VM usage, especially during idle times, to control costs AKA Pay-as-you-go – AI workloads on cloud VMs, especially those requiring GPUs, may be expensive to run continuously
Security & Privacy – VMs are secure & comply with local data privacy regs i.e. GDPR, HIPAA etc. – Hypervisor vulnerabilities do not expose AI workloads/DB due to unauthorized access.
Backup and Disaster Recovery – Regular back up of trained models & datasets – Develop DR strategies to quickly restore AI workloads in case of VM or cloud service provider outages.
AI-Specific Hardware Requirements – Confirm availability of VMs with specialized hardware i.e. GPUs or TPUs if AI models require them, not all cloud regions or Cloud service providers offer same hardware, which can limit flexibility.
Storage Bandwidth – Many AI Apps often need to process large datasets, VMs may face data I/O bottlenecks when reading or writing to storage, especially with lower-performing virtual disks.