Local AI moves the work—and the trade-offs—to your machine

Start with the job, then choose where it runs

Running an AI model locally can change where information is processed. It also puts more of the responsibility for computing resources and maintenance on whoever operates the machine. The useful question is whether that arrangement suits a particular job.

In its documentation, Ollama says it does not see prompts or data when users run models locally. It also provides a setting to disable its cloud models and web-search features. Those statements describe Ollama's documented behavior, not every application that might connect to it. [1]

The surrounding workflow matters too. An interface, optional web tool, conversation store or backup service may send information elsewhere. Before calling a workflow local, map where its inputs and outputs actually go.

The resources still have to come from somewhere

Ollama documents that a model can occupy GPU memory, system memory or a combination of both. Its memory requirements also depend on context length and parallel requests. A model that loads successfully may therefore behave differently when given longer documents or several requests at once. [1]

The cost comparison should include hardware, electricity, setup, maintenance and time spent correcting output. Avoiding a per-request inference charge does not make those costs disappear. An existing computer may be enough for a narrow task, but that is something to test rather than assume.

A small trial can make the decision clearer

Consider a hypothetical business that wants to extract a supplier name, document date, reference number and total from incoming documents. Before committing to a local or cloud-based system, it could create a small set of representative, non-sensitive samples and write down the correct values.

The comparison could then track:

• Accuracy: which fields are correct, missing or invented.
• Turnaround: the time to produce an output and correct it.
• Data flow: which parts of the document leave the machine and which services receive them.
• Maintenance: who would handle installation, updates and failures.

The business should also decide how missing or ambiguous information will be handled. For this trial, an explicit “not found” would be preferable to an invented reference number. Human checking would remain part of the proposed workflow.

The result might justify a limited local tool, a cloud service or further testing. This is a suggested decision process, not a performance test conducted by The Illustral.

Local and cloud can coexist

Apple's June 2024 description of its foundation models provides a historical example of a hybrid design: an on-device language model alongside a larger server-based model. That example illustrates a design choice, not Apple's current model lineup. [2]

Different tasks can justify different execution locations. For a small team, the arrangement also needs to be maintainable: someone must understand its boundaries and be able to respond when it stops working as expected.