Setting Up Ollama (Power User)

Snipset includes powerful AI capabilities, such as AI Chat, AI Snippet Generation, and Semantic Search (OmniSearch). This guide covers the Advanced, CPU-optimized installation for power users who want to save disk space and don’t need GPU acceleration.

Why use the CPU-Optimized version?

Ollama detects GPU availability dynamically. If the GPU libraries are removed, Ollama automatically falls back to the CPU using the ggml-cpu-*.dll library. Since Snipset uses cloud models and lightweight embedding models, you don’t need local GPU processing. Removing these libraries saves up to ~1.75 GB of storage space.

Beginner? If you want the fastest and easiest installation method, please see our Beginner’s Getting Started Guide instead.

Step 1 — Install Custom Ollama

1. Download the ZIP Version

   Go to

   Ollama GitHub Releases

   , find the latest version, and download the ollama-windows-amd64.zip file.

2. Extract the Folder

   Extract the zip file and save the folder in your Local Disk C (e.g., C:\ollama-windows-amd64).

3. Remove GPU Libraries (Storage Optimization)

   Open PowerShell and run the following commands to remove the GPU folders (saving ~1.75 GB):

   Remove-Item -Recurse -Force “C:\ollama-windows-amd64\lib\ollama\cuda_v12” Remove-Item
   -Recurse -Force “C:\ollama-windows-amd64\lib\ollama\cuda_v13” Remove-Item -Recurse -Force
   “C:\ollama-windows-amd64\lib\ollama\vulkan”

   Alternative for WSL users:

   rm -rf “/mnt/c/ollama-windows-amd64/lib/ollama/cuda_v12” rm -rf
   “/mnt/c/ollama-windows-amd64/lib/ollama/cuda_v13” rm -rf
   “/mnt/c/ollama-windows-amd64/lib/ollama/vulkan”

   (The remaining size of the Ollama folder is now only about 150 MB.)

4. Register to Environment Variable (Path)

   So your computer can detect the ollama command:

   • Open the Start Menu, search for Environment Variables and select “Edit the system environment variables”.

   • Click the Environment Variables… button.

   • Under System variables or User variables, find the Path variable, then click Edit…

   • Click New and enter your folder path: C:\ollama-windows-amd64

   • Click OK on all windows to save.

Step 2 — Pull the Required Models

Open your terminal and pull the desired models:

Model 1: The Chat Model

ollama pull gemma4:31b-cloud

Model 2: The Embedding Model

ollama pull nomic-embed-text

Step 3 — Verify Ollama is Running

• Open your web browser and navigate to http://localhost:11434

• If Ollama is running correctly, you will see: “Ollama is running”.

Step 4 — Connect Snipset to Ollama

1. Go to Snipset → Settings → AI Configuration tab.

2. Ensure Base URL is http://localhost:11434.

3. Verify the green Online indicator.

4. Select your Chat Model and Embed Model from the dropdowns.