Choosing hosting for AI workloads: GPU, network and cost
Separate inference from training, verify VRAM, network and pricing transparency.
Start with the workload profile: training, fine tuning, or inference. Interactive services need low latency and stability more than peak power, while training requires larger VRAM and higher throughput. These inputs eliminate unsuitable plans early.
Verify GPU models and driver versions. Check CUDA and cuDNN availability, container support, and images. Version mismatch causes downtime, so prepare a dependency list and validate it on a staging host.
Evaluate storage and network: local NVMe for datasets, fast scratch space, and read write performance. For large data, outbound traffic pricing and egress limits matter, otherwise budgets can double.
Ask about GPU availability. Clarify reservations, queues, launch limits, and SLA for hardware replacement. Production systems need predictable availability and maintenance windows.
Request GPU metrics: temperature, throttling, ECC errors, and power limits. Without observability, it is hard to distinguish model regression from hardware issues. Strong providers supply dashboards and Prometheus exports.
Security and compliance still matter. Confirm physical data location, encryption options, and tenant isolation. For sensitive data, dedicated nodes or isolated VPCs are safer.
Run a test before scaling: measure throughput, tokens per second, and cost per task. Compare pricing per GPU hour and validate autoscaling behavior. This avoids expensive trial and error.
Do not ignore licensing and model usage limits. Some datasets require separate approvals and environment separation, which affects region choice and isolation.
If scaling by time of day is expected, evaluate GPU availability in those hours and startup time. Warm up time can be critical for user facing scenarios.
Compare providers by support depth: CUDA expertise, network guidance, and access to engineers. For AI projects, competence matters as much as hardware.
Put requirements into one document and send it to several providers. Differences in responses reveal maturity and help pick a long term partner.