Skip the Cloud, Not the Control: Running AI Models Locally with Docker Model Runner

Source: Dev.to

Docker Model Runner enables you to run powerful AI models locally using the same Docker CLI tools you already trust in production.

Why Local‑First AI Matters

Cloud‑based LLM APIs are convenient, but they come with trade‑offs:

• 💸 Token costs add up quickly
• 🔒 Sensitive data leaves your machine
• 🌐 Latency and rate limits slow iteration
• ⚙️ Limited control over model behavior

Running models locally flips that equation. You keep full ownership of your data, avoid per‑request costs, and iterate faster—especially during development and testing.

Docker Model Runner Overview

Docker Model Runner lets you run AI models locally with familiar Docker commands. Models are packaged and distributed as OCI artifacts, so they work seamlessly with existing Docker infrastructure such as Docker Hub, Docker Compose, and CI pipelines.

Supported Features

• Any OCI‑compliant registry
• Popular open‑source LLMs
• OpenAI‑compatible APIs for easy app integration
• Native GPU acceleration for high‑performance inference

All without reinventing your toolchain. If you already use Docker, you’re 90 % of the way there.

Running a Model

docker model run 

Docker Model Runner pulls the model from an OCI registry, initializes it locally, and exposes an inference endpoint you can start using immediately.

• No Python environments
• No custom scripts
• No fragile dependencies

For a full walkthrough, see the [Docker Model Runner Quick Start Guide].

Model Catalog & OCI Workflow

• Explore a curated catalog of open‑source AI models on [Docker Hub]
• Pull models directly from [Hugging Face] using OCI‑compatible workflows

Because models are OCI artifacts, they are:

• Versioned
• Portable
• Easy to share across teams

This makes collaboration and reproducibility dramatically simpler.

OpenAI‑Compatible APIs

Docker Model Runner supports OpenAI‑compatible APIs, so many existing apps work out of the box. You can connect it to frameworks like:

• Spring AI
• LangChain
• OpenWebUI

Your app talks to a local endpoint but behaves as if it’s using a hosted API, making the switch between local development and production painless.

GPU Acceleration

For teams with capable hardware, Docker Model Runner offers native GPU acceleration, unlocking fast, efficient inference on your local machine.

• No manual CUDA setup
• No driver gymnastics

Just Docker abstracting the complexity. Learn more about GPU support in [Docker Desktop].

Scaling Across Teams

Docker Model Runner is designed to scale:

• Use Docker Compose for multi‑service applications
• Integrate with Testcontainers for AI‑powered testing
• Package and publish models securely to Docker Hub
• Manage access and permissions for enterprise teams

Because it’s Docker‑native, it fits naturally into CI/CD pipelines and existing governance models.

Ideal Use Cases

Docker Model Runner shines when you want to:

• Prototype AI features without cloud costs
• Keep sensitive data fully local
• Test models before production deployment
• Standardize AI workflows across teams
• Avoid vendor lock‑in

If you already trust Docker in production, this is the missing piece for AI. Local AI doesn’t have to be complicated.

Getting Started

With Docker Model Runner you can:

• Run LLMs locally
• Keep control of your data
• Cut costs
• Use the Docker tools you already know

👉 [Try Docker Model Runner] and bring AI development into your local workflow.
Hassle‑free local inference starts here 🚀