Together AI Dedicated Endpoints Skill for Agents

What it is

Together AI Dedicated Endpoints Skill is a configuration package that teaches AI coding agents how to provision and manage dedicated GPU inference endpoints on Together AI. It covers creating endpoints with specific hardware (H100, H200, A100), configuring autoscaling, and managing the endpoint lifecycle through the Together API.

This skill targets teams deploying LLMs for production inference at scale who need single-tenant GPU resources with no rate limits and custom model configurations. It works with Claude Code, Cursor, and Codex CLI.

How it saves time or tokens

The skill encodes Together AI's API patterns, hardware options, and best practices directly into the agent's context. Instead of reading documentation and writing boilerplate API calls, the agent generates correct endpoint provisioning code on the first attempt. Autoscaling configuration ensures you pay only for active inference capacity.

How to use

1. Install the skill: npx skills add togethercomputer/skills.
2. Ask your AI coding agent to create a dedicated endpoint for a specific model.
3. The agent generates Python code using the Together SDK with correct hardware and scaling parameters.

Example

from together import Together

client = Together()

# Create a dedicated endpoint
endpoint = client.endpoints.create(
    model='meta-llama/Llama-3.3-70B-Instruct-Turbo',
    hardware='gpu-h100-80gb',
    min_replicas=1,
    max_replicas=4,
    autoscale=True,
)
print(f'Endpoint URL: {endpoint.url}')

# Scale the endpoint
client.endpoints.update(endpoint.id, min_replicas=2)

# Check status
status = client.endpoints.get(endpoint.id)
print(f'Status: {status.state}')

Related on TokRepo

• AI Tools for API -- explore API tools for model deployment and inference
• AI Tools for Agents -- discover skills and tools for building AI agents

Common pitfalls

• Dedicated endpoints incur per-hour costs regardless of request volume; scale down min_replicas during off-peak hours.
• Model availability varies by hardware type; check Together AI's model compatibility matrix before selecting GPU hardware.
• Autoscaling has a cold start delay when scaling from zero replicas; keep min_replicas at 1 for latency-sensitive applications.