A sleek developer's workspace with multiple screens, AI interfaces, and a futuristic, high-tech environment. Icons for Open WebUI, Ollama, and LM Studio are subtly integrated into the scene.

How to Run Large Language Models (LLMs) Locally: A Guide to Getting Started

November 24, 2024

9 min read

Large Language Models (LLMs) are transforming the way we interact with technology, providing powerful tools for generating text, answering questions, and automating workflows. While cloud-based solutions like OpenAI's ChatGPT are popular, running LLMs locally is becoming increasingly accessible. This approach offers unique advantages, including enhanced privacy, reduced latency, and cost efficiency.

In this post, we’ll explore how to run LLMs locally, highlight popular tools like Ollama, Open WebUI, LM Studio, and AnythingLLM, and dive into the benefits of keeping your AI in-house.

Why Run LLMs Locally?

Running LLMs locally is gaining traction due to the following benefits:

  1. Enhanced Privacy
    With local deployment, your data never leaves your machine. This is crucial for sensitive applications such as medical research, legal analysis, or personal projects.

  2. Lower Latency
    Cloud-based models rely on internet connectivity and server response times. Local LLMs, on the other hand, provide near-instant responses since everything is processed on your machine.

  3. Cost Savings
    Paying for cloud services or API calls can add up, especially for high-frequency usage. Running LLMs locally eliminates recurring fees and keeps costs predictable.

  4. Customizability
    You have full control over how the model operates, including finetuning it for specific tasks or industries.

Getting Started with Local LLMs

Deploying LLMs locally can seem intimidating at first, but modern tools make it straightforward. Here are some of the top options:

1. Ollama

Ollama is a robust solution designed for seamless local LLM deployment. It offers:

  • An easy-to-use command-line interface for downloading and running models like LLaMA and GPT-based variants.
  • Efficient resource management, making it suitable even for consumer-grade hardware.
  • Built-in support for privacy, ensuring no data is sent to external servers.

To get started:

# Install Ollama
brew install ollama

# Download a model
ollama pull llama2

# Run the model
ollama run llama2

2. Open WebUI

Open WebUI provides a web-based interface for running and interacting with LLMs locally. It supports a range of models and includes features like:

  • Multi-platform support (Linux, macOS, and Windows).
  • A browser-based interface for user-friendly access.
  • Fine-tuning and model switching capabilities.

3. LM Studio

LM Studio is perfect for developers who want a graphical frontend for running LLMs. It supports both CPU and GPU-based inference, offering:

  • Simple model installation.
  • Monitoring tools for performance optimization.
  • A polished GUI for non-technical users.

4. AnythingLLM

AnythingLLM is a flexible framework designed for custom local LLM implementations. It allows developers to:

  • Integrate LLMs into specific workflows or applications.
  • Extend functionality through plugins.
  • Experiment with various models in a modular environment.

How to Choose the Right Tool

The right tool depends on your goals and hardware:

  • If you want a command-line-first solution with privacy by default, Ollama is a great choice.
  • For a GUI-based experience, try LM Studio.
  • If you need a customizable web-based solution, Open WebUI is ideal.
  • Developers looking for integration and extension capabilities should explore AnythingLLM.

Hardware Requirements for Running LLMs Locally

The hardware you’ll need depends on the model size:

  • Consumer-grade setups (8-16 GB RAM): Use quantized versions of models like LLaMA 2 or GPT-based derivatives.
  • High-end systems (32+ GB RAM, powerful GPUs): Deploy larger models for better accuracy and more complex tasks.
  • Edge devices: Some tools, like Ollama, allow running lightweight models even on laptops.

Use Cases for Local LLMs

Running LLMs locally opens up a world of possibilities:

  • Content Creation: Generate blog posts, social media content, or personalized emails.
  • Coding Assistance: Debug code, generate snippets, or refactor codebases with privacy.
  • Knowledge Bases: Train models on proprietary data for in-house Q&A.
  • Automation: Build custom workflows and tools powered by local AI.

Wrapping it up

Running LLMs locally empowers you to leverage cutting-edge AI while maintaining control over your data and costs. Tools like Ollama, Open WebUI, LM Studio, and AnythingLLM make this easier than ever. Whether you’re a hobbyist or a professional, deploying AI locally provides unmatched flexibility and privacy.

Photo of author (Travis)
Travis