# Local vs cloud AI models: which jobs go where

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Canonical page: https://hatchedstacks.com/library/local-vs-cloud-ai-models
Last checked: 2026-10-22

Send hard reasoning, long writing and customer-facing work that must be excellent to a cloud model, and keep private data, high-volume small jobs and anything that must work offline on a local model on your own machine. Most businesses end up with both, and the split is decided job by job, not once for the whole stack.

Start from your data and your volume.

| If you are | Start with |
| --- | --- |
| A business with no private data and light volume | A dedicated always-on machine, then cloud models for everything and a local model added later |
| A practice whose notes and documents are private | A dedicated always-on machine with enough memory for a local model, and a rule that private jobs stay local |
| A business with thousands of small jobs a day, such as tags, titles and sorting | A dedicated always-on machine running a small local model for the bulk work, with a cloud model for the hard jobs |
| A developer building for clients | A dedicated always-on machine for each client, then a written job-by-job table of local and cloud |

## Decide per job, not once

There is no single answer for a whole business. Summarizing a private meeting and writing a sales page are different jobs, and they often belong on different models.

Ask four questions about each job, in this order. The first yes decides.

**Figure: Local or cloud: four questions**

1. Does the job touch private data? Yes: Local model.
2. Must it work with no internet connection? Yes: Local model.
3. Does it need hard reasoning, or will a customer read the result? Yes: Cloud model.
4. Is it a small job you run many times a day? Yes: Local model.

None of the above: Either. Test both and keep the cheaper one that passes.

**Figure: Common jobs and where they usually go**

| Job | Private data | Volume | Quality needed | Usual home |
| --- | --- | --- | --- | --- |
| Drafting customer replies | Sometimes | Medium | High | Cloud, or local when the thread is private |
| Summarizing meeting notes | Often | Medium | Medium | Local |
| Tags and titles | Rarely | High | Low | Local |
| Questions over private documents | Yes | Medium | Medium | Local |
| Coding | Sometimes | Medium | High | Cloud |
| Embeddings for search | Often | High | Medium | Local |
| Long reports | Sometimes | Low | High | Cloud |
| Image and video generation | Rarely | Low | High | Cloud |

## What cloud models do best

Hard reasoning, long documents, careful use of tools, and writing that goes out under your name. The strongest models run in the cloud. You pay per use through an API, or per seat through a plan, and prices change often enough that you should read them on the provider's page on the day you choose.

## What local models do best

Private data, work that has to carry on offline, bulk small jobs such as tags, titles and sorting, and embeddings for search over your own files. You pay once for the hardware and then for electricity, so there is no bill per call.

## The quality gap, honestly

For a narrow job, a local model that passes your test is good enough for that job, whatever the benchmarks say about it in general.

Build the test from your own work: a small set of real examples, each with the answer you would accept, run on both models. A chat that feels clever is not a test.

## Memory decides which local models fit

A local model has to fit in the machine's memory, with room left for everything else the stack runs. On a Mac with Apple silicon, the main processor and the graphics processor share one pool of memory, so the memory size is the number to check.

On 2026-10-22, Apple's spec pages listed the Mac mini with up to 64GB of unified memory and the Mac Studio with up to 512GB. Quantization, which stores a model with less precision, lets a larger model fit in less memory, at some cost in quality.

## Private data: catch it first, keep it local

Decide what counts as private before any job runs: client names, health details, money, anything under a confidentiality agreement. Then set a rule that sends any job holding it to a local model, whatever model was asked for, and write down every place data goes when it does leave the machine.

Check where your local runner actually runs each model. Ollama, for example, offers models in the cloud as well as on your own machine, so an app on your desk is not proof that the model is.

## A harness is what makes a local model useful

A local model on its own is a chat box. The harness around it gives it tools, memory and rules, and a local server lets every agent on the machine call it the way it would call a cloud model.

LM Studio, for example, can run a local server that accepts requests in the same format as OpenAI's and Anthropic's APIs, so tools built for those can point at it instead. Ollama runs on macOS, Linux and Windows. Under most runners sits an engine such as llama.cpp, which treats Apple silicon as a first-class target, or Apple's own MLX framework.

## Routing between the two

Once each job has a home, routing does the rest. Every call passes the private-data check first, then goes to the cheapest model that passed your test for that job.

**Figure: How a call finds its model**

1. A job comes in
2. Private-data check: private work goes straight to a local model
3. Router: picks the cheapest model that passed your test for this job
4. Local or cloud model: the routing log records which, and the cost

## What goes wrong

- A local model so large it leaves no memory for the rest of the stack, and everything slows down.
- Private notes sent to a cloud model just once, to see if it does better.
- Local models judged on how a chat feels, instead of on your own jobs.
- A runner set to a cloud model when you thought the work was staying local.

## Questions

**Is a local model as good as ChatGPT?**

Not at everything. For a narrow job, such as tagging or summarizing your own notes, a local model can pass the same test a cloud model passes. Test it on your work and decide job by job.

**Do you need a graphics card for local models?**

On a Mac with Apple silicon there is no separate card: the graphics processor shares the main memory, so memory size decides which models fit. On a PC, the memory on the graphics card usually decides it.

## Terms on this page

[Local model](https://hatchedstacks.com/library/ai-stack-glossary#local-model), [Cloud model](https://hatchedstacks.com/library/ai-stack-glossary#cloud-model), [Model routing](https://hatchedstacks.com/library/ai-stack-glossary#model-routing), [Embeddings](https://hatchedstacks.com/library/ai-stack-glossary#embeddings), [Force-local rule](https://hatchedstacks.com/library/ai-stack-glossary#force-local-rule), [Harness](https://hatchedstacks.com/library/agent-vs-harness)

## Related guides

- Builds on: [Rent or own your AI stack](https://hatchedstacks.com/library/rent-or-own-your-ai-stack.md)

## Sources

- [Ollama](https://ollama.com/), checked 2026-10-22
- [Ollama: Download](https://ollama.com/download), checked 2026-10-22
- [LM Studio: Developer docs](https://lmstudio.ai/docs/developer), checked 2026-10-22
- [llama.cpp](https://github.com/ggml-org/llama.cpp), checked 2026-10-22
- [MLX](https://github.com/ml-explore/mlx), checked 2026-10-22
- [Apple: Mac mini tech specs](https://www.apple.com/mac-mini/specs/), checked 2026-10-22
- [Apple: Mac Studio tech specs](https://www.apple.com/mac-studio/specs/), checked 2026-10-22