Put AI where the work belongs

Your AI strategy needs a placement decision.

On the device. In a private cloud. In a public cloud.

Each has a role.

The right choice starts with the work.

On October 16, NVIDIA announced RTX Spark laptops designed to run AI models and agents locally.

It is another reason to evaluate where AI should run before deciding what to buy.

Consider three practical examples:

• On the device: A field technician uses a local model to search downloaded equipment manuals at a site with unreliable connectivity. Test response quality, battery life, and offline functionality.

• Private cloud: An engineering team shares an AI assistant across proprietary designs, service records, and internal documentation. Dedicated infrastructure can provide shared capacity and greater control over deployment and data access. It also requires clear responsibility for operations, security, and upgrades.

• Public cloud: A customer-service application needs to handle seasonal demand spikes or access a larger managed model. Elastic capacity can help, while data policies, service availability, and usage costs still need attention.

These are illustrative placement choices. The same workflow may use all three.

My takeawayChoose placement by workload, data sensitivity, performance, operating capability, and total cost.

Running locally does not automatically make AI private. A private cloud does not automatically make it cheaper. Public-cloud flexibility still requires cost control.

Start with one workflow.

Test the complete path from data to result, including the network.

Put AI where the work belongs.

Which workflow in your enterprise would benefit from a different placement?