Together AI: Deploy Any Hugging Face Model Instantly

Developers can now deploy and run virtually any model from Hugging Face thanks to Together AI's new Dedicated Container Inference (DCI) offering. This advancement significantly lowers the barrier to entry for experimenting with cutting-edge AI models, abstracting away the complexities of inference server configuration and container setup.

The rapid pace of AI model releases, such as Netflix's recent void-model, often creates a lag between discovery and practical application. Traditionally, integrating a new model involves substantial effort in environment setup, dependency management, and inference server configuration. Together AI's DCI aims to eliminate this delay.

Using agents like Goose and Together's dedicated containers skill, developers can reportedly go from identifying a model to having a running inference container in a single session. For instance, deploying Netflix's void-model involved installing the skill, issuing a single prompt to the agent, and receiving a complete, runnable setup for Together's infrastructure.

Seamless Model Deployment

The process is streamlined into a few core steps: installing the necessary skill, initiating a deployment prompt with the model's Hugging Face URL, and allowing the agent to handle the rest. This includes fetching model details, configuring the inference server, and generating deployment files.

Once deployed, the Together CLI provides tools to test the model. For void-model, this involved submitting a payload with video data and a prompt, receiving a request ID, and later polling for the processed video output. This asynchronous process is managed entirely by the platform.

This capability democratizes access to advanced AI models, enabling immediate experimentation without requiring deep infrastructure expertise. Teams can now react instantly to new model releases, integrating them into workflows with minimal friction.

Why Dedicated Container Inference?

Together AI's DCI provides a private, GPU-accelerated environment tailored for specific models. Unlike shared resources, DCI offers dedicated compute, removing the need for developers to manage their own GPU clusters, such as those for AI inference. The flexibility allows for the deployment of any model, not just those pre-approved on a managed platform.

This approach is particularly beneficial for organizations aiming to move quickly. It bypasses the typical delays associated with provisioning VMs, resolving inference server dependencies, or waiting for managed endpoint support. The pay-as-you-go cost model further encourages experimentation by focusing on actual usage without infrastructure overhead.

Teams interested in leveraging Together AI's Dedicated Container Inference can reach out to the company for setup. The platform supports a range of AI workflows, from training and fine-tuning to large-scale inference.