Host AI Models Locally: A Step-by-Step Guide Using Ollama & Open WebUI

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Have you ever wondered how your phone knows what you're typing before you finish the sentence? Or how your favorite streaming service recommends shows based on what you've watched? These are examples of artificial intelligence (AI) and machine learning (ML) in action. AI and ML are everywhere these days, making our lives easier and more efficient.

Now, imagine this: instead of relying on distant servers in the cloud to perform these tasks, you could host these AI models right on your own computer. That's what we'll discuss when I say "host AI models locally." It means running these intelligent systems directly on our PC or device, bringing the power of AI closer to us.

Why is this becoming so popular? Well, with more people using smart devices and applications that require real-time decision-making, there's a growing demand for faster and more private solutions. By hosting AI models locally, you can enjoy several benefits. For one, it enhances privacy because our data doesn't need to be sent over the internet. It also reduces latency, meaning tasks are completed quicker since computations happen right where we are. Plus, it can save costs associated with cloud services.

In this blog post, we'll explore how hosting AI models locally works, its advantages, and who might benefit from it. Whether you're a tech enthusiast or someone looking to understand more about AI, this guide will provide insights that are both informative and accessible.

What is Hosting AI Models Locally?

Hosting AI models locally means running these intelligent systems directly on our personal computer or device, rather than relying on remote servers in the cloud. It's like having all the power of AI right at our fingertips without needing an internet connection.

This approach differs from cloud-based hosting, where data and tasks are processed on distant servers. Local hosting avoids the delays caused by network traffic and distance, providing a faster and more efficient experience.

Examples of AI models that can run locally include chatbots used in messaging apps for instant responses and image classifiers that recognize objects in photos without needing server processing.

Why Host AI Models Locally?

Hosting AI models locally offers several advantages:
  1. Privacy and Data Security: Our data remains on our device, reducing the risk of security breaches.
  2. Faster Inference Times: Local processing eliminates latency, ideal for real-time applications like gaming or autonomous vehicles.
  3. Cost Savings: Avoid expensive cloud services, making it a budget-friendly option.
  4. Offline Functionality: Operate AI models without internet access, suitable for environments with unreliable connectivity.
However, hosting locally requires sufficient hardware, such as powerful CPUs or GPUs, which might be a barrier for some users. Scalability can also be challenging compared to cloud solutions. Hence we can host the distilled version of the LLM models and to decide which distilled version we can host locally the guide is as follows:
  1. 8GB RAM can host 1.5B LLM
  2. 16GB RAM can host 7B LLM
  3. 18GB RAM can host 8B LLM
  4. 32GB RAM can host 14B LLM
  5. 161GB RAM can host 70B LLM
  6. 1,342GB RAM can host 671B LLM

Who Should Host AI Models Locally?

  1. Developers: Ideal for testing and prototyping AI/ML projects without external dependencies.
  2. Small Businesses and Startups: Save costs while maintaining data control.
  3. Hobbyists and Enthusiasts: Engage in hands-on AI experimentation and learning.
  4. Organizations Prioritizing Data Sovereignty: Ensure compliance with data regulations by keeping data local.
In summary, hosting AI models locally provides privacy, speed, cost savings, and offline capabilities, making it an attractive option for developers, small businesses, hobbyists, and organizations focused on data control.

To host AI models locally, I'll be using Ollama & Open WebUI. Before we start, let's understand what is Ollama & Open WebUI?

Ollama

Ollama is an open-source platform designed to make running large language models (LLMs) accessible to everyone, regardless of their technical expertise or hardware resources. It acts as a bridge between complex AI models and our local machine or server by:
  1. Simplifying Model Hosting: Ollama lets us run state-of-the-art AI models like Llama, DeepSeek, and others directly on our computer without requiring expensive GPU hardware or cloud services. This makes it an excellent choice for developers, researchers, and even casual users who want to experiment with AI.
  2. Performance Optimization: Ollama is lightweight and fast. It’s designed to optimize resource usage (CPU and memory), making it possible to run large models efficiently on standard hardware.
  3. Multi-Model Support: We can host multiple AI models in a single instance of Ollama, allowing us to switch between different models or use them for various tasks (e.g., one model for text generation and another for code completion).
  4. Ease of Use: Ollama provides a user-friendly web interface where we can pull models, manage them, and generate outputs without needing to write code or understand complex configurations.

Open WebUI

Open Web UI is a lightweight, open-source web interface that provides an easy-to-use dashboard for interacting with AI models. It’s designed to work seamlessly with local or remote AI services like Ollama, or even APIs like OpenAI (if you want to connect to external services). The key features of Open Web UI include:
  1. User-Friendly Interface: A clean and intuitive web interface where we can chat with AI models, view model status, and manage our configurations.
  2. Multi-Model Support: We can integrate multiple AI services (not just Ollama) into a single dashboard.
  3. Localhost-Focused: It’s optimized for running on our local machine, making it ideal for privacy-conscious users who prefer to keep their data on their own devices.

Why Use Open WebUI with Ollama?

Open Web UI integration with Ollama offers:
  1. Simplified Access: Instead of dealing with multiple interfaces (Ollama’s web UI and the command line), Open Web UI acts as a centralized hub.
  2. Enhanced Functionality: Open Web UI adds features like:
    1. Multi-model support.
    2. Easy switching between models.
    3. Integration with third-party AI services (optional).
  3. Security: Since everything runs locally, our data remains private and secure.

Step-by-step guide to install Ollama & Open WebUI

  1. A modern laptop with sufficient RAM (at least 4GB recommended)
  2. Administrator privileges on your system.
  3. Basic understanding of command-line operations.
  4. Install Python: Ollama requires Python to run locally.
  5. Install Ollama locally based on your operating systems
  6. Install Open WebUI locally

Demo


Benchmarks

To evaluate the model’s performance on my local machine, I conducted benchmarks using ollama-bench to measures tokens per second and total processing time. Here are the results:

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