Run Powerful Language Models Locally: Your Laptop as an AI Hub

Simon Willison’s USB Stick Survival Plan

Simon Willison has prepared for a potential collapse of human civilization by loading open-weight large language models (LLMs) onto a USB stick. These models, publicly shared by their creators, can be downloaded and run locally on personal hardware. Willison envisions this as a condensed, albeit imperfect, version of Wikipedia that could help reboot society.

Growing Community of Local LLM Enthusiasts

Running LLMs on personal devices is becoming increasingly popular. The subreddit r/LocalLLaMA boasts over half a million members who are interested in local LLMs. Local models appeal to those who prioritize privacy, desire independence from major AI providers, or simply enjoy experimenting with technology.

Lowering the Barrier to Entry

Previously, running effective LLMs required expensive GPUs and powerful servers. However, advances in model optimization have made it possible to run these models on laptops and even smartphones. Willison notes that personal computers can now handle models once thought to require $50,000 server racks.

Privacy and Control Benefits

Using online LLMs like ChatGPT often means your conversations can be used for training purposes, raising privacy concerns. OpenAI and Google train their models on user interactions, sometimes without straightforward opt-out options. Local LLMs allow users to maintain privacy and retain full control over their AI experience. Additionally, local models provide consistent behavior unlike online models, which may change unpredictably due to provider updates.

Limitations and Learning Opportunities

Local models tend to be less powerful and more prone to hallucinations compared to their larger online counterparts. However, this can help users develop a better intuition about the strengths and weaknesses of AI models.

Getting Started with Local LLMs

For users comfortable with command-line interfaces, tools like Ollama enable easy downloading and running of hundreds of models with simple commands. For those preferring graphical user interfaces, LM Studio offers a user-friendly environment to browse, select, and interact with models from Hugging Face.

Hardware Requirements and Practical Examples

Each billion model parameters roughly require 1 GB of RAM. A 16 GB laptop can run large models like Alibaba’s Qwen3 14B by closing other applications. Smaller models can run on smartphones; for instance, Meta’s Llama 3.2 1B runs on an iPhone 12 using the LLM Farm app, although with limited reliability.

The Joy of Experimentation

While not everyone needs to run local LLMs, the experience can be rewarding and fun, providing both practical utility and a deeper understanding of AI technology. Willison emphasizes the enjoyment many find in exploring these models on their own devices.