Local LLMs Without the Cloud: Ollama in a Tauri App

A practical guide to building native desktop developer tools that run 100% offline and keep your data private.

Privacy and independence from cloud API subscriptions are the cornerstone of every project we build at AI-Software.

In this article, I walk through the architecture of our native tools, which combine a lightweight Tauri (Rust) front end with a local Ollama or llama.cpp daemon.

⚡ Key takeaways:
🔒 Code confidentiality: no prompt, log or code snippet ever leaves your local NVMe drive.
💰 No per-token billing: Llama 3, Qwen 2.5 and Mistral models run at full GPU speed on consumer cards with 8–16 GB of VRAM.
✈️ Offline operation: reliable work on planes, on trains and in air-gapped industrial networks.

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1. Recommended local models: VRAM vs quality

Here is a cheat sheet of the models that work best for day-to-day development:

Model	Quantization	Required VRAM	Best use case
Qwen 2.5 Coder 7B	Q4_K_M	5.2 GB	Generating and refactoring Rust/TypeScript code
Llama 3.2 3B Instruct	Q8_0	3.8 GB	Fast summaries, JSON formatting
Mistral NeMo 12B	Q4_K_M	8.4 GB	Advanced reasoning and documentation
DeepSeek Coder V2 Lite	Q5_K_M	11.2 GB	Complex architectural and algorithmic tasks

A local LLM integrated into a desktop user interface

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2. Streaming tokens with Tauri events (emit/listen)

The Rust backend asynchronously (Tokio) reads the token stream from Ollama and publishes each token immediately as a Tauri event (emit). The React component subscribes to that event with listen from @tauri-apps/api/event, so the text appears in the UI in real time:

code snippet

// Receive the token stream from the Ollama daemon and forward it to the UI as Tauri events
let mut stream = client.generate_stream(request).await?;
while let Some(res) = stream.next().await {
    let token = res?.response;
    app_handle.emit("llm-token", token)?;
}

Developer workstation with real-time AI token streaming

More ready-to-use integration scripts are available in our Scripts Library.

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3. Workstation requirements

There is no separate “Ollama + Tauri” product — this is a pattern I use across our desktop tools (including the optional local VLM in the video sorter).

Component	Practical minimum
Runtime	Ollama or llama.cpp on localhost
VRAM	Match the quantization from the table in section 1 to your GPU
Network	Bind the daemon to localhost; never expose it on a public interface
UI	Tauri 2.x plus Tauri events (`emit` in Rust, `listen` in React) — see the sketch above

4. How to put it together

Install Ollama and pull one model from the table (start with 3B or 7B, not 12B).
Check that localhost:11434 responds from a terminal before you wire up the UI.
In Tauri, stream tokens through the Rust backend and Tauri events, not through a browser fetch to a cloud API.
Keep prompts and logs on disk — no “diagnostic” telemetry.

Helper scripts: Scripts Library. Tool page with an optional local VLM: VideoSorterLocal.