Some things shouldn't leave your computer. Medical notes, legal drafts, journal entries, messages to your therapist, that honest performance review you're still editing. When you paste those into a cloud AI service, they travel to a server you don't control, get processed by infrastructure you can't inspect, and may end up in training data you never agreed to.
That bothered me enough to build local LLM support into WindowSill. And one of the easiest way to set it up is with Ollama, a free tool that runs AI models directly on your hardware.
This guide walks you through the setup. By the end, you'll have grammar checking, text rewriting, tone adjustment, and translation running locally in any Windows app, with zero data leaving your machine.
No GPU required. Ollama runs on CPU too, just slower. If you have an NVIDIA GPU with 6+ GB of VRAM, responses will be noticeably faster.
Download Ollama from ollama.com and run the installer. Once installed, Ollama runs as a background service on your machine. You can verify it's working by opening a terminal and running:
ollama --version
If you see a version number, you're good.
Not all models are equal for writing tasks. You want something that understands grammar, tone, and natural language well. Here are three solid options:
| Model | Size | Best for | Pull command |
|---|---|---|---|
| Deepseek R1 8B | ~8 GB | General writing, grammar, rewriting | ollama pull deepseek-r1:8b |
| Qwen 3.5 4B | ~4.5 GB | Lighter machines, still capable | ollama pull qwen3.5:4b |
| Deepseek R1 1.5B | ~1 GB | Fast responses, good grammar | ollama pull deepseek-r1:1.5b |
Open a terminal and pull whichever model fits your hardware:
ollama pull qwen3.5:4b
This downloads the model to your machine. It only needs to happen once.
A note on model size vs. quality: Larger models (13B+ parameters) produce better writing output, but they need more RAM and a decent GPU to run at a usable speed. For most writing tasks (grammar fixes, tone adjustments, short rewrites), an 8B model is more than enough. Start small and upgrade if you need to.
Ollama exposes a local API at http://localhost:11434. WindowSill can connect to it automatically.
In WindowSill:
http://localhost:11434qwen3.5:4b)That's it. WindowSill now routes AI requests to Ollama instead of a cloud service.
Open any app where you write (Word, Outlook, Notion, Slack, a browser, anything). Type a sentence with a deliberate mistake:
Their going to the meeting tommorrow at 3pm, can you confirmed?
Select the text. WindowSill's Analyze / Rewrite sill should appear on the bar. Hit the Spell Check option.
If everything is connected, the corrected text will come back after a few seconds:
They're going to the meeting tomorrow at 3 PM. Can you confirm?
The first request might take a moment while Ollama loads the model into memory. Subsequent requests will be faster.
Once connected, all of WindowSill's AI writing features work through your local model:
Grammar and spell check. Select text in any app, fix errors without opening a separate tool.
Rewriting. Highlight a paragraph and ask for a rewrite. Useful for polishing drafts or simplifying dense writing.
Tone adjustment. Switch between professional, casual, and attention-grabbing tones. You can also create custom tone presets for recurring needs (e.g., "customer support reply" or "executive summary").
Translation. Select text and translate to any of 35+ supported languages. The quality depends on the model you chose. Llama 3 handles common language pairs (English/Spanish, English/French, English/German) well. For less common pairs, a larger model or a specialized translation model works better.
Custom prompts. Build reusable prompts with variable injection. For example, a prompt that takes selected text and converts it into a formatted meeting recap with today's date auto-inserted.
Summarization. Select a long email or document section and get a summary.
All of this happens on your machine. Nothing goes to OpenAI, Google, or anyone else.
Local models are slower than cloud APIs. Here's how to keep things comfortable:
ollama stop qwen3.5:4b in a terminal.I want to be honest about the trade-offs. Local models are good, but they're not as capable as the latest cloud models for every task. Here's where I'd still use a cloud provider:
The good news: you don't have to pick one. WindowSill supports both local and cloud providers at the same time. You can route sensitive content through Ollama and non-sensitive content through a cloud API. Per-prompt model selection makes this easy.
Yes. Ollama runs models on your hardware and exposes only a local API (localhost:11434). No data is sent to external servers. You can verify this by disconnecting from the internet and confirming the AI features still work.
For grammar and spell checking, Deepseek R1 8B offers the best balance of quality and speed. Qwen 3.5 4B is a close second and slightly faster. Deepseek R1 1.5B works for basic grammar but occasionally misses subtle errors.
Yes. WindowSill supports multiple AI providers simultaneously. You can configure Ollama for sensitive tasks and a cloud provider like OpenAI for non-sensitive tasks, then select which model to use on a per-prompt basis.
A single 7-8B parameter model takes about 4-5 GB of disk space. If you want multiple models available, plan for 10-15 GB. Models are stored in Ollama's data directory and can be removed with ollama rm <model-name>.
Yes. Ollama supports CPU-only inference. It's slower (5-15 seconds per response instead of 1-3 seconds with a GPU), but it works. Any modern CPU from the last few years can handle it.
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