Ollama Tutorial: Run Local AI Models + Ollama Cloud Setup (Full Guide)

About This Video

Learn how to set up Ollama and run local AI models on your own computer, plus how to use Ollama Cloud models when you need more power than your GPU can handle. This step-by-step Ollama tutorial covers installation on Windows, macOS and Linux, pulling and running models, switching between local and cloud models, and connecting Ollama to your own apps through its API. Ollama lets you run open-source LLMs like Llama, Gemma, Qwen and DeepSeek completely offline and private — no subscription and no data leaving your machine. Ollama Cloud runs the same models on remote hardware using the exact same commands, so you can run large models that won't fit on a laptop. ⏱️ TIMESTAMPS 00:00 Intro — what Ollama is and why use it 00:20 Installing Ollama (Windows / Mac / Linux) 00:40 Running your first local AI model 01:00 Choosing the right model for your hardware (RAM & VRAM) 01:20 Essential Ollama commands (pull, run, list, rm, ps) 01:30 Ollama Cloud models — signing in and running them 01:40 Local vs cloud: when to use which 02:30 Using the Ollama API + API keys 03:00 Connecting Ollama to other tools 03:15 Troubleshooting common errors 03:30 Final thoughts ❓ QUICK ANSWERS What is Ollama? Ollama is a free tool that downloads and runs open-source large language models locally on your own computer through a simple command line or API. Is Ollama free? Yes, Ollama and local models are free. Cloud models require a free ollama.com account, with usage limits on the free tier. What are Ollama Cloud models? Cloud models look and behave like local models, but the work is offloaded to Ollama's servers, so you can run large models without a powerful GPU. Do I need a GPU for Ollama? No. Small models (1B–8B) run on CPU and 8–16 GB of RAM, though a GPU makes them much faster. For big models, use cloud models. Is Ollama private? Local models run fully offline and nothing is sent anywhere. Cloud models send your prompts to Ollama's servers, and Ollama can be locked to local-only mode if you prefer. 🔗 LINKS Ollama download: https://ollama.com/download Model library: https://ollama.com/search Docs: https://docs.ollama.com 👍 If this helped, like and subscribe for more local AI and self-hosted AI tutorials.

#ollama#localai#llm#aimodels#ollamacloud

Related Videos

OmniRoute Setup on Mac: Connect Claude Code for Free AI Tokens
Watch Tutorial

OmniRoute Setup on Mac: Connect Claude Code for Free AI Tokens

earn how to install and configure OmniRoute on macOS to route Claude Code through free AI model endpoints and avoid token rate limits. Complete Terminal guide. <p class="lead">OmniRoute is an open-source local AI gateway that lets you aggregate free-tier provider models behind a single OpenAI-compatible endpoint. Follow this step-by-step guide to install OmniRoute on macOS and integrate it with Claude Code to bypass usage limits.</p> <h2>Prerequisites</h2> <ul> <li>A Mac running macOS Big Sur or newer (Intel or Apple Silicon M1/M2/M3/M4)</li> <li>Node.js (v18 or higher recommended)</li> <li>Terminal app (or iTerm2)</li> </ul> <h2>Step 1: Install OmniRoute Globally on Mac</h2> <p>Open your macOS Terminal and execute the global npm installation command:</p> <code>npm i -g omniroute</code> <h2>Step 2: Start the OmniRoute Service</h2> <p>Launch the OmniRoute gateway server locally:</p> <code>omniroute</code> <p>This starts the gateway on your local machine (defaulting to port 20128) and prints the local dashboard URL: http://localhost:20128.</p> <h2>Step 3: Connect Free Providers in the Dashboard</h2> <ol> <li>Open http://localhost:20128 in your browser.</li> <li>Navigate to the Providers tab.</li> <li>Connect your preferred free-tier API providers (such as Gemini, Groq, Kimi, GLM, or DeepSeek).</li> <li>Go to the API Keys section and generate a local token for authentication.</li> </ol> <h2>Step 4: Configure Claude Code to Route Through OmniRoute</h2> <p>To direct Claude Code CLI traffic through your local OmniRoute instance, set the Anthropic environment variables in your active shell or ~/.zshrc file:</p> <code>export ANTHROPIC_BASE_URL="http://localhost:20128"</code><br> <code>export ANTHROPIC_AUTH_TOKEN="your_omniroute_api_key"</code><br> <code>export CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY=1</code> <h2>Step 5: Verify the Connection</h2> <p>Run a test query in Claude Code to verify requests are reaching your gateway:</p> <code>claude "Hello, confirm connection status."</code> <h2>Useful Resources</h2> <ul> <li><a href="https://github.com/diegosouzapw/OmniRoute" target="_blank" rel="noopener">OmniRoute Official GitHub Repository</a></li> <li><a href="https://nodejs.org/" target="_blank" rel="noopener">Download Node.js for Mac</a></li> <li><a href="https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview" target="_blank" rel="noopener">Claude Code Official Documentation</a></li> </ul>

Oct 8, 2026
Read →
Use GPT 6 Astra FREE in Codex (Free Unlimited Codex with GPT 6)
Watch Tutorial

Use GPT 6 Astra FREE in Codex (Free Unlimited Codex with GPT 6)

In this video, I'm walking you through how to set up and configure the GPT-6 Astra model with your local development environment using Flagship Router. This is an educational tutorial about local model configuration and development tool management—all standard practices that developers use for their workflows. 📌 What This Video Covers: Cloning the Flagship Router repository from the official source Installing Node.js and project dependencies with npm install Running the launch command to build and start the local router Logging into the Flagship Router web interface Turning off the API key requirement for smoother operation Exploring the model library—336 models available with 86 ready to use Finding and testing the GPT-6 Astra model Copying the model identifier for configuration Using the CLI Tools section to configure your development application Applying the model configuration and launching your app Verifying the setup with a live test prompt Confirming the connection through Flagship Router ⚙️ How It Works: Flagship Router is a local routing tool that helps developers manage connections between their coding applications and various model providers. In this tutorial, I demonstrate how to configure it with the GPT-6 Astra model and integrate it with development tools using standard local proxy techniques. 🛠️ Prerequisites: Node.js installed on your system Git installed for cloning the repository Basic familiarity with terminal or command prompt A development application or code editor installed 🔗 Resources Mentioned: Flagship Router Repository: https://github.com/theRizwan/FlagshipRouter Node.js Official Website: https://nodejs.org 📝 Disclaimer: This video is for educational and informational purposes only. It demonstrates standard local development environment configuration techniques using open-source tools and models. The tools and methods shown are intended for personal learning and legitimate development workflows. Always ensure you comply with the terms of service of any software or platform you use. 💬 Have questions about the setup or ran into an issue? Drop a comment below and I'll personally help you out. 🔔 Subscribe to Rizwan for more coding tutorials, development environment setups, and tech guides every week. #GPT6Astra #FlagshipRouter #LocalDevelopment #CodingSetup #AIModels #ProgrammingTutorial #Rizwan

Oct 6, 2026
Read →
I Tested 1B-1.7B AI Models Locally — Here Is What Happened
Watch Tutorial

I Tested 1B-1.7B AI Models Locally — Here Is What Happened

I ran Claude on 1B–1.7B local AI models to see if they could actually handle coding tasks on my 24GB RAM machine. Testing small language models is the best way to understand the limits of local hardware. Monitoring system performance while pushing these tiny models to their breaking point reveals if they can maintain efficiency without killing CPU or memory resources. Whether you are using a Claude coding agent or exploring general LLM inference, knowing which model size fits your specific setup is key. The results from these tests highlight which configurations work and where the performance falls off. Subscribe for weekly AI performance breakdowns, and let me know in the comments if you want to see a test with larger parameter sizes next.

Oct 2, 2026
Read →