Fara-7B
Microsoft · United States · MIT
Commercial use: Yes — free for commercial use
MIT. No conditions.
A small model that can look at a screen and click things, running on your own machine.
Why it should matter to you
The most common automation blocker we meet is a system with no API and no export. Traditionally that means screen-scraping scripts that break whenever the interface changes. A local computer-use model is the first credible alternative, and running it locally matters because the alternative is streaming your internal systems to someone else.
How it connects to our work
We treat these as constrained tools, never as unattended workers. A misclick in a records system is a data-integrity incident, so the design question is always blast radius and rollback before capability.
From our field notesThe 90-second integration feasibility test any business owner can run.
Advantages
- A small agentic model built for computer use and UI interaction rather than chat
- MIT, and small enough to run beside the application it is driving
- Runs locally, so the screen contents it reads never leave the machine
Disadvantages
- Narrow: it is a computer-use model, not a general assistant
- Early, with a limited track record
- Computer-use agents fail in ways that are hard to detect automatically — a wrong click looks like a successful step
Reach for it when
Automating a legacy desktop or web application that has no API, locally, without screenshotting your systems to a third party.
Where it falls down
Unattended operation. Computer-use agents need a constrained blast radius and human checkpoints; the failure mode is silent and expensive.
Running it
8 GB GPU or any 16 GB Mac. See the hardware sizing tables for how that maps to specific chips and cards, and the quantisation guide for what you give up at each bit width.
Jurisdiction: United States
Best raw capability and the deepest tooling ecosystem. For EU personal data you are relying on a transfer framework rather than on data never leaving the bloc, so check whether your DPA and your customers accept that.
Watch for: Enterprise API tiers usually promise no training on your data; consumer tiers and free tiers frequently do not. The free tier is where this bites.
Frequently asked
Can I use Fara-7B commercially?
MIT. No conditions.
What hardware do I need to run Fara-7B?
8 GB GPU or any 16 GB Mac. Weights alone are roughly 4.5 GB at Q4_K_M and 8 GB at Q8_0. Add KV cache on top of that, which grows with your context length.
What licence is Fara-7B released under?
MIT. Full commercial use. Shortest and least restrictive of the common licences; no explicit patent grant.
Where can I use Fara-7B for free?
Self-hosting the weights is the free route. 8 GB GPU or any 16 GB Mac.
Similar models
Gemma 4 31B
24 GB GPU at Q4_K_M, or a 32 GB Mac.
Llama 4 Scout 17B
80 GB GPU or 96 GB+ Mac at Q4_K_M.
gpt-oss-120b
80 GB GPU, or 96 GB+ unified memory.
gpt-oss-20b
16 GB GPU or Mac. A strong default for local agents.
Wiring Fara-7B into something real?
We build the evaluation harness, the failover and the cost ceilings around a model like this, so it survives contact with production.
Maps to AI agent development, AI automation development and AI strategy and consulting. Or see it working: our case studies.