How to Run Ollama Alongside ChatGPT and Claude — In One Tab

Ollama is one of the best tools in the AI ecosystem right now. It lets you run powerful open-source models — Llama 3, Mistral, Phi-3 — entirely on your own machine, completely free, with no API keys and no data leaving your computer.

The problem: switching between a local Ollama model and cloud AIs like ChatGPT or Gemini means constant tab-switching, copy-pasting, and context loss. It kills the workflow that makes using multiple models valuable in the first place.

This guide shows you how to run Ollama alongside cloud AIs in a single interface, comparing their outputs side by side. The whole setup takes about 10 minutes and costs nothing.

What You Need

• Ollama — free, open source, available at ollama.com. Runs on Mac, Windows, and Linux.
• A free API key — Gemini API via Google AI Studio is completely free to start, no credit card required. OpenAI and Anthropic also offer API access if you prefer GPT-4o or Claude.
• AI Hub — free, no account needed, runs in your browser at aihubdash.com/dashboard/.

You don't need all three — even just Ollama plus one free Gemini key gives you a powerful local-plus-cloud setup at zero cost.

Step 1 — Install Ollama and Pull a Model

Download Ollama from ollama.com and run the installer. Once installed, open a terminal and pull your first model:

ollama pull llama3

This downloads Meta's Llama 3 (8B parameter version by default — about 4.7GB). It's a strong general-purpose model that handles most everyday tasks well. If you want alternatives:

ollama pull mistral     # Mistral 7B — fast, efficient, good at instruction following
ollama pull phi3        # Microsoft Phi-3 — smaller, very fast, surprisingly capable

After pulling, start the Ollama server:

ollama serve

This runs Ollama as a local API server on http://localhost:11434. Keep this terminal window open while you're using it. On Mac, Ollama also runs as a menu bar app automatically after installation — in that case you may not need to run ollama serve manually.

To verify it's running, visit http://localhost:11434 in your browser. You should see a plain text response confirming Ollama is active.

Step 2 — Open AI Hub and Connect Ollama

Open aihubdash.com/dashboard/ in your browser. When the setup panel appears:

1. Find the Ollama section and toggle it On.
2. Click Detect Models — AI Hub will query your local Ollama server and automatically find the models you've pulled (like llama3).
3. Select which models you want active. Llama 3 will appear in the list if you pulled it in Step 1.

AI Hub connects directly to your localhost — no data is sent to any server. Your local model conversations stay entirely on your machine.

Step 3 — Add a Cloud AI Alongside It

Now add a cloud model to run in parallel. The easiest free option:

1. Go to aistudio.google.com and sign in with a Google account.
2. Click Get API Key — it's free, and the free tier is generous enough for most use.
3. Copy your key and paste it into the Gemini field in AI Hub's setup panel.

Now you have a local model (Llama 3, running on your machine) and a cloud model (Gemini, running on Google's servers) active at the same time in one interface.

If you already have OpenAI or Anthropic API keys, you can add those too. The setup is the same — paste the key into the corresponding field.

Why Run Both?

Local and cloud models have genuinely different strengths, and combining them makes both more useful:

Local Ollama models are:

• Completely free — no API costs, no rate limits
• Private — your prompts never leave your machine
• Available offline — no internet required once downloaded
• Fast for shorter responses — no network latency

Cloud models are:

• More capable on complex reasoning tasks
• Better at nuanced writing and instruction following
• More up-to-date on recent information
• Don't require GPU hardware to run quickly

Together, they cover each other's weaknesses. Use local for quick iterations, sensitive data you don't want sent to a third party, or when you're offline. Use cloud for the tasks that need more reasoning depth.

Example Workflow

Here's a practical pattern that works well for coding tasks:

Broadcast a question like "How should I structure error handling for async API calls in this Node.js function?" to both Llama 3 (local) and Gemini (cloud) simultaneously.

Llama 3 will often give you a fast, workable first answer — a concrete pattern you can implement immediately. Gemini will typically give a deeper response that explains the tradeoffs between approaches, or catches edge cases the local model missed.

Reading both side by side takes 30 seconds and gives you more signal than either model alone. You're not choosing between them — you're using them as two different angles on the same problem.

The same pattern works for writing, research, and brainstorming. Local for speed and privacy; cloud for depth. Together in one tab with no switching required.