How-to

How to write unit tests with a local AI model

Last updated 2026-11-04

What you need first for “how to write unit tests with a local ai model”

Apple Silicon Mac, macOS 15 or later, and for how to write unit tests with a local ai model the unified-memory minimum that the chosen tier requires (6 GB for Nano, 12 GB for Lite, 16 GB for Quick (24 GB to run it fully in memory), 24 GB for Core and Vision 3.8, 64 GB for Plus). For write unit tests with local ai, those are the minimums to load a tier; a long agent session needs more. You need the internet to download each model (and once to activate a Pro key); after that, chat works offline.

Steps

1. Open the function under test. Drag the file onto the chat window or use the project chip.
2. Ask for one focused test at a time. Local tier quality holds well for one test; quality on long batches drops.
3. Iterate inline. Run the suggested test in your terminal; paste failures back.
4. Pin the tier to Lite or Core. Quick is not the tier for agent-driven code work (it produced no patch in any of 50 blind SWE-bench Verified tries) and it decodes slower than Lite, 43.6 against 53.4 tok/s on an M1 Ultra.

What is the specific thing to know about this guide?

For test scaffolding the Lite tier is usually the right speed-quality balance on a 16 GB Mac, with Core reserved for trickier or long-context work.

Ask for one test at a time and the local tiers hold up well; ask for a whole suite in one turn and quality falls off sharply as the response lengthens. The practical loop is narrow: name the function, name the single behaviour you want covered, run what comes back, and paste the failure in unedited. Failures are the most useful thing you can give a smaller model, because they replace guesswork about your codebase with the actual error text. Keep the function under test in view, a test written against a signature the model inferred rather than read is the common way this goes wrong.

What can go wrong with this guide?

• Lite’s code quality is good, not great; tricky tests sometimes need a manual second pass.
• Long batches of tests in one prompt drop quality; break them up.

How does this guide differ from the cloud equivalent?

Hosted test-writing tools log your function-under-test alongside the prompt. Outlier’s local path leaves no log on a third-party server.

What does this guide not claim about “how to write unit tests with a local ai model”?

This guide does not claim feature parity with cloud-side workflows for “how to write unit tests with a local ai model”. Specifically, the product surface covers chat, file attachment, the local agent loop, project scoping, and the model picker. Cross-device sync, team workspaces, and shared session history are out of scope and are not on the v1.9 backlog either.

What is the right tier for test writing?

Lite at 5 GB, 12 GB RAM minimum, 53.4 tok/s on M1 Ultra reference. That is the baseline. If the function under test is short and the test framework is well-known (pytest, vitest, jest), Lite handles it. For tricky fixtures or property-based testing, Core (24 GB RAM) is the upgrade.

Where does this guide fit in the rest of the lineup?

Lite is good enough for most pytest scaffolds; Core is the upgrade for tricky fixtures or property-based tests. Quick is not the tier for agent-driven test work: it produced no patch in any of 50 blind SWE-bench Verified tries, and it decodes slower than Lite.

One unique number

4 steps. Once the model is downloaded your chats and files stay on your Mac. Recommended pattern: one test per turn, paste the failure into the next turn, iterate until green.

Download Outlier for Mac

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Free · M1 or newer · macOS 15+ (free update) · 276 MB. Intel Macs are not supported.