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together-dedicated-model-inference

Skill

Deploy and operate models on dedicated GPUs with Together AI's Dedicated Model Inference (DMI, the v2 dedicated endpoints API): beta endpoints, deployments, deployment profiles and hardware configs, autoscaling, traffic splitting, A/B tests, shadow experiments, Prometheus metrics, and custom model o

0Hardware

Install

git clone https://github.com/togethercomputer/skills.git ~/.claude/skills/together-dedicated-model-inference

What is together-dedicated-model-inference?

Deploy and operate models on dedicated GPUs with Together AI's Dedicated Model Inference (DMI, the v2 dedicated endpoints API): beta endpoints, deployments, deployment profiles and hardware configs, autoscaling, traffic splitting, A/B tests, shadow experiments, Prometheus metrics, and custom model or LoRA adapter uploads. Reach for it whenever the user mentions together beta endpoints or tg beta commands, client.beta.endpoints, DMI resources like ep_/dep_/cr_/ml_ IDs, or wants production model serving with traffic management on Together AI. This is the current dedicated-hosting API and also covers migrating off the retired legacy v1 endpoints API (non-beta client.endpoints / together endpoints), whose create and restart now return HTTP 403.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~188 tokens of context used while enabled, before you invoke anything

Documentation

README · ~6 min read

Together Dedicated Model Inference

Overview

Dedicated model inference (DMI) serves a model on reserved single-tenant GPUs. It bills per minute per running replica (by hardware, not by token), has no hard rate limits, and uses the same inference API as serverless models.

The resource model has six parts: Project → Model → Config → Endpoint → Deployment → Replica.

  • Endpoint — stable inference name; routes traffic across its deployments by weight.
  • Deployment — binds one model + one config to an endpoint with an autoscaling policy; runs the replicas.
  • Config — immutable published revision describing how the model runs (engine, GPU type

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