Infrastructure decision
Local AI vs Cloud GPU: Which Should You Use?
Local AI hardware is strongest when you value ownership, predictable capacity and local data control; cloud GPUs are strongest when you need elastic capacity, remote availability or hardware you do not want to own. The better choice depends on utilization, model size, uptime and operating constraints.
Compare total cost, not hourly price against purchase price
For local hardware, include purchase cost, useful life, electricity, backup, connectivity and maintenance. For cloud GPU, include instance time, storage, bandwidth, idle capacity and managed services. Use the same monthly workload assumptions on both sides.
Privacy and control are architecture decisions
Local execution can keep inference and data on hardware you control, but that transfers patching, physical security, backups and uptime to you. Cloud infrastructure transfers some physical and capacity management to the provider while adding provider, account and network dependencies.
Hybrid is a legitimate third option
Persistent lightweight agents can run on inexpensive always-on infrastructure while GPU-heavy inference is invoked only when needed, or sensitive workloads can stay local while burst workloads use cloud capacity. Document which component runs where.
Run both sides in one comparison
Use the dedicated calculator to compare local hardware amortization and power against GPU cloud hourly cost using the same workload assumptions.
Compare local vs cloud cost →Sources
- NVIDIA CUDA Programming Guide — GPU memory— GPU memory architecture
- Ollama — FAQ— Local inference memory and concurrency behavior
Agent Infrastructure Recommendation Kit
Make the local-vs-cloud decision with a repeatable process
The Agent Infrastructure Recommendation Kit gives you a six-step system to define requirements, estimate capacity, compare local, cloud, VPS, GPU, and hybrid options, document evidence, and make the recommendation.
Get the Recommendation Kit — $19