How to Configure Raycast AI with Ollama for Offline Local Models on macOS

For software engineers working under strict data governance, corporate intellectual property mandates, or non-disclosure agreements, uploading proprietary source code to cloud-hosted language models (such as OpenAI GPT-4o or Anthropic Claude 3.5 Sonnet) is often strictly prohibited. Yet losing keyboard-driven desktop AI shortcuts can significantly reduce development velocity.

Running open-weight models locally on macOS through Ollama and linking them directly into Raycast AI provides a zero-leakage workflow. Raycast acts as the system-wide keyboard interface across Quick AI, AI Chat, and Custom AI Commands, while Ollama executes inference entirely on your Mac's Apple Silicon unified memory without sending any prompt text or code diffs over the internet.

Architecture: Zero Cloud Data Transmission

Understanding the data boundary is critical for corporate compliance:

• Local Inference Loop: When configured with local models, Raycast sends prompt payloads directly over an HTTP connection to the local Ollama daemon (by default listening on http://localhost:11434). Model weights run in local RAM/VRAM via Metal acceleration. Responses stream straight back into the Raycast interface.
• Zero Third-Party Logging: Neither Raycast's cloud infrastructure nor model vendors receive your prompts, code snippets, or completions.
• Air-Gapped Operation: Once model weights are downloaded, the entire stack operates without active internet connectivity. No Raycast AI credits are consumed for local text generations.

Hardware Sizing & Recommended Coding Models

Because local inference relies on your Mac's Unified Memory Architecture (UMA), selecting models appropriate for your hardware profile ensures smooth multitasking alongside IDEs like Xcode, VS Code, and local Docker containers.

Context Window Note: Raycast caps local model context at 64k tokens to protect system memory and ensure stable streaming performance on macOS.

Step-by-Step Setup Guide

Step 1: Install Ollama and Pull Local Models

You can install Ollama on macOS through Homebrew or the official standalone app:

# Option A: Install via Homebrew
brew install --cask ollama

# Option B: Or download the DMG from ollama.com and drag to /Applications

Launch the Ollama application once to ensure the menu bar agent and system binary links are established.

Next, open your macOS Terminal and pull your chosen programming models:

# Pull premier coding models
ollama pull qwen2.5-coder:7b
ollama pull llama3.1:8b

# Verify installed models
ollama list

You can test that the model responds locally:

ollama run qwen2.5-coder:7b "Write a Python function to compute moving averages."

Step 2: Configure Local Models in Raycast

Raycast provides first-party integration for Ollama models within its native AI engine (available on Raycast Pro):

1. Press ⌥ Space (or your Raycast hotkey) to open Raycast.
2. Press ⌘ , to open Raycast Settings.
3. Select the AI tab from the top navigation bar.
4. Go to Models & Providers and scroll down to the Local Models section.
5. Raycast will automatically detect your local Ollama installation. If your pulled models do not immediately appear, click Sync Models.
6. You can also download new models directly inside Raycast: enter the model tag (for example, qwen2.5-coder:7b) in the Install Ollama Model field and click Install.

Note: Raycast launches the local Ollama background server on demand when you trigger a local query, so you do not need to manually keep terminal windows open.

Step 3: Remote Ollama Host (Optional for Team Servers)

If your team runs an on-premise Mac Studio or Linux GPU server located inside your corporate LAN or private VPN:

1. Leave the server running Ollama configured to bind its IP address (OLLAMA_HOST=0.0.0.0:11434).
2. In Raycast Settings → AI → Models & Providers → Local Models, find the Ollama Host field.
3. Enter your private internal endpoint, such as http://192.168.1.50:11434.
4. Click Sync Models. Requests will route to your private internal server without traversing the public internet.

Privacy Hardening: Compliance Settings Checklist

To guarantee that confidential company data cannot leave your Mac under any circumstances, audit these specific Raycast settings:

1. Disable AI Cloud Sync:
   • Navigate to Raycast Settings → Cloud Sync.
   • If enabled, Raycast encrypts and syncs AI Chat transcripts across devices. If your enterprise security compliance strictly bans cloud storage of raw chat history, disable AI Cloud Sync or regularly clear your chat logs.
2. Review AI Personalization & Memory:
   • In Settings → AI → Personalization, review the Memory feature.
   • When global AI Memory is active, background summaries of conversational history may occasionally use Raycast-hosted models for background extraction. For strict air-gapped security, turn off Memory.
3. Avoid Cloud-Only Tools:
   • Server-hosted utilities such as AI image generation require a cloud model round-trip and use AI credits. Keep your local workflows focused on text and code generation.
4. Decline Diagnostic Feedback Submissions:
   • Never use "Send Feedback" on chat threads that contain confidential code, as feedback submissions transmit the active prompt context to Raycast support for debugging.

Essential Developer Shortcuts and Custom AI Commands

Once configured, local models can be embedded directly into macOS development workflows.

1. Dedicated Coding Assistant in AI Chat

Open AI Chat in Raycast (⌘ ⌥ Space or your configured hotkey). In the bottom model picker or by typing /model, select your local model (e.g., Local: qwen2.5-coder:7b).

You can now paste proprietary code, database schemas, and stack traces with full confidence that inference occurs entirely within your Mac's RAM.

2. Custom AI Commands for Codebases

Go to Raycast Settings → Extensions → AI → AI Commands and click Create AI Command. You can bind each command directly to your local model:

• Generate Conventional Commit:
  • Prompt: Write a concise git commit message conforming to the Conventional Commits specification based on the following git diff. Output only the commit message subject and body without markdown wrappers:
  • Input: Selected Text or Clipboard
  • Model: Local: qwen2.5-coder:7b
  • Action: Paste into Active App or Copy to Clipboard
• Security & Vulnerability Audit:
  • Prompt: Analyze the following code for memory safety issues, injection risks, concurrency bugs, and credential leaks. Highlight high-severity vulnerabilities first with concrete patch proposals:
  • Input: Selected Text
  • Model: Local: qwen2.5-coder:14b
  • Action: Show in Chat
• Type Signature & Docstring Generator:
  • Prompt: Add strict TypeScript type annotations and TSDoc comments to the following code without altering runtime logic:
  • Input: Selected Text
  • Model: Local: qwen2.5-coder:7b
  • Action: Replace Selected Text

Alternative for Raycast Free Users: Community Extensions

Native integration under Raycast's Local Models tab is a Raycast Pro feature. If you use the free version of Raycast, you can still access local Ollama models via community extensions from the Raycast Store:

1. Search for Ollama AI in the Raycast Store.
2. Install the extension and configure the host URL to http://localhost:11434.
3. Use commands like Ollama Chat, Ask Ollama, or text transformation actions. While this operates through an extension view rather than the native system-wide Quick AI popup, it retains identical zero-leakage local offline privacy.

Troubleshooting Local Models in Raycast

• Raycast reports "Ollama is not installed": Ensure the Ollama application exists in /Applications or that /usr/local/bin/ollama (or /opt/homebrew/bin/ollama) is accessible in your environment PATH. Launch the Ollama app once from Finder, then click Sync Models in Raycast Settings.
• Model not showing in model picker: Open Terminal and run ollama list to confirm the model tag. If it is present, return to Raycast Settings → AI → Models & Providers → Local Models and click Sync Models. Check Manage Models to ensure the model checkbox is toggled on.
• High latency or system sluggishness: Running a model that exceeds available unified memory triggers aggressive macOS swap to SSD. If you have a 16GB Mac, avoid 14B or 32B models; stick to 7B or 8B Q4 quantized variants. Close memory-heavy browser tabs or Docker containers before running extended multi-turn reasoning prompts.

References

• Local Models | Raycast Manual
• Raycast AI Privacy & Security | Raycast Manual
• AI | Raycast Manual
• Ollama
• Download Ollama