DMR – Buchatech.com

🎤 I’m excited to share that I’ll be returning to BITCON in a week! I will be speaking at BITCON 2025, a gathering focused on Black voices in technology, innovation, and community. You can check out the full speaker lineup here: BITCON 2025 Speakers. The conference this year is virtual and its free. You can check out the site here: https://bitcon.blacksintechnology.net

The conference has a ton of great speakers lined up from some of the largest tech companies such as Google, Microsoft, and more. And to top it off the keynote this year is Kelsey Hightower! You dont want to miss this one.

My Session: “The Easiest Way to Run LLMs Locally: Meet Docker Model Runner”

Docker Captain: Steve Buchanan DMR session

At BITCON, I’ll be presenting “The Easiest Way to Run LLMs Locally: Meet Docker Model Runner”. In this session, I’ll look at:

Why run LLMs locally? The benefits in terms of cost, privacy, latency, and control
How Docker Model Runner simplifies things — containerizing large models, managing dependencies, and lowering friction
Demo and walkthrough — showing you step by step how to get a model up and running on your own machine or server
Best practices, pitfalls, and tips — what I’ve learned building and deploying these systems
Q&A / hands-on help — to get you started with your own setup

My goal is that attendees leave with a concrete, reproducible process they can apply right away.

Why It Matters

Large language models (LLMs) are powerful, but running them locally has often felt out of reach for smaller teams, indie devs, or people in resource-constrained environments. With the right tooling (like Docker Model Runner), we can lower that barrier—unlocking more experimentation, more privacy, and more control over where and how inference happens.

I believe this aligns well with the mission of BITCON: elevating voices, demystifying advanced tech, and making it accessible. I hope this talk helps bridge a gap for folks who want to explore AI locally without getting lost in infrastructure.

I am excited to be speaking at BITCON again. To learn more about my session check it out here:

BITCon Session: The Easiest Way to Run LLMs Locally: Meet Docker Model Runner

BITCON is free! Be sure to register today: HERE

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