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Private AI Processing

Give your team access to generative AI on infrastructure you control.

Using several AI models should not require someone to manage each machine by hand. Shared processing connects media requests from different workspaces to the hardware and models they need.

From a team request to a usable result
From a team request to a usable result Workspaces submit jobs to a coordinator. It queues work for private processing workers, which load models from the model library. Outputs are collected and checked before a resumable download to the originating workspace. WorkspacesRequests & reference mediaCoordinatorShared queue & schedulingPrivate workersSpeech · image · music · videoOutput checksCollect, decode & verifyModel libraryLoad the required weights LoadResumable download to the requesting workspace

From a team request to a usable result
From a team request to a usable result Workspaces submit jobs to a coordinator. It queues work for private processing workers, which load models from the model library. Outputs are collected and checked before a resumable download to the originating workspace. WorkspacesRequests & reference mediaCoordinatorShared queue & schedulingPrivate workersSpeech · image · music · videoOutput checksCollect, decode & verifyModel libraryLoad the required weights LoadResumable download to the requesting workspace

Features

  • Multimedia requests

    Submit supported speech, image, music, and video jobs with their reference files from your own workspace.

  • Model loading

    Stage the required model on a processing machine, then switch workloads through a shared coordinator.

  • Shared scheduling

    Queue work from multiple people and assign it to available hardware without each person managing the machines.

  • Recoverable delivery

    Check output files and resume interrupted downloads into the workspace that submitted the request.

How it works

A coordinator queues multimedia jobs, loads the required models, and collects their outputs. Local model serving keeps processing on team-controlled hardware; the same capacity can support a background LLM runtime between media workloads.

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