Local development for applications powered by LLMs is gaining momentum, and for good reason. It offers several advantages on key dimensions such as performance, cost, and data privacy. But today, local setup is complex.
Developers are often forced to manually integrate multiple tools, configure environments, and manage models separately from container workflows. Running a model varies by platform and depends on available hardware. Model storage is fragmented because there is no standard way to store, share, or serve models.
The result? Rising cloud inference costs and a disjoined developer experience. With our first release, we’re focused on reducing that friction, making local model execution simpler, faster, and easier to fit into the way developers already build.
Docker Model Runner is designed to make AI model execution as simple as running a container.
With Docker Model Runner, running AI models locally is now as simple as running any other service in your inner loop. Docker Model Runner delivers this by including an inference engine as part of Docker Desktop, built on top of llama.cpp and accessible through the familiar OpenAI API. No extra tools, no extra setup, and no disconnected workflows. Everything stays in one place, so you can test and iterate quickly, right on your machine.
Enabling GPU acceleration (Apple silicon)
GPU acceleration on Apple silicon helps developers get fast inference and the most out of their local hardware. By using host-based execution, we avoid the performance limitations of running models inside virtual machines. This translates to faster inference, smoother testing, and better feedback loops.
https://www.youtube.com/watch?v=rGGZJT3ZCvo
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