Deploy Any Hugging Face Model Instantly with Goose and Together DCI

Rongchai Wang May 08, 2026 20:36

Together's Dedicated Container Inference lets developers deploy any Hugging Face model, like Netflix's Void-Model, in minutes using Goose.

Deploy Any Hugging Face Model Instantly with Goose and Together DCI

Deploying machine learning models often involves navigating a maze of setup complexity: configuring inference servers, setting up container environments, and understanding model-specific requirements. Together.ai is aiming to eliminate those barriers with its Dedicated Container Inference (DCI) platform, allowing developers to deploy any Hugging Face model in production-ready GPU environments with minimal effort.

The process leverages Goose, a command-line interface (CLI) agent runner, alongside Together's DCI infrastructure. The result? A seamless deployment experience that skips the usual setup headaches.

How it Works

Consider Netflix’s recently released Void-Model, which removes objects from videos while accounting for their interactions with the environment. Traditionally, deploying such a model would require days of setup. With Together’s tools, developer Blaine Kasten was able to deploy it on release day in just three steps:

Install the Together DCI skill: Using the command npx skills add togethercomputer/skills, Goose gains the ability to configure Together's infrastructure for any model.
Run a single command: A simple prompt like I want to deploy this model on Together’s dedicated containers https://huggingface.co/netflix/void-model initiates the entire deployment process.
Let the agent handle the rest: Goose automatically configures the inference server, generates container files, and deploys the model, producing a working setup hosted on Together infrastructure.

The output of this process was a fully functional repository, available on GitHub, that anyone can use to run Void-Model.

Why Dedicated Container Inference Matters

Together’s DCI platform provides developers with private, GPU-backed environments to run models, eliminating the need to manage shared resources or configure clusters. This flexibility is key for teams that want to act quickly when new models are released, like those from Netflix or the open-source community.

Additionally, the pay-as-you-go pricing model makes experimentation accessible. Developers can try out models without committing significant resources to infrastructure or enduring lengthy setup times.

What’s Next?

For developers interested in cutting-edge AI, Together’s DCI offers a clear path to rapid experimentation and deployment. Whether testing models like Netflix’s Void-Model or developing new applications, the combination of Goose and DCI transforms what used to be a technical bottleneck into a streamlined process.

To explore Together DCI further, visit Together’s website.

Image source: Shutterstock