I’ve been spending a lot of time blogging on Pluralsight lately, and one of my recent posts covered a topic I’m genuinely excited about: running large language models (LLMs) locally. Specifically, I explored a tool called Docker Model Runner that makes this process more accessible for developers.
In the post, I broke down a few key ideas.
Why Run an LLM Locally
There’s a lot of momentum around cloud-hosted AI services, but running models locally still has its place. For many developers it means more control, quicker experimentation, and the ability to work outside of a cloud provider’s ecosystem.
Tools in This Space
Before zeroing in on Docker Model Runner, I broke down other ways developers are running models locally. The landscape is quickly evolving, and each tool has trade-offs in terms of usability, performance, and compatibility with different models.
Why Docker Model Runner
What really stood out to me with Docker Model Runner is how it lowers the barrier to entry. Instead of wrestling with environment setup, dependencies, and GPU drivers, you can pull down a container and get straight to experimenting. It leans into Docker’s strengths of portability and consistency, so whether you’re on a desktop, laptop, or even testing in a lab environment, the experience is smooth and repeatable.
For developers who are curious about LLMs but don’t want to get bogged down in infrastructure, this tool is a great starting point.
If you want the full breakdown and step-by-step details, you can check out my Pluralsight blog here:
👉 https://www.pluralsight.com/resources/blog/ai-and-data/how-run-llm-locally-desktop
