What is This Skill?
This skill teaches AI coding agents how to provision and manage Together AI dedicated endpoints. Deploy models on single-tenant GPUs with autoscaling, no rate limits, and custom configurations for production workloads.
Answer-Ready: Together AI Dedicated Endpoints Skill for coding agents. Single-tenant GPU inference with autoscaling and no rate limits. Custom model configs and production deployment. Part of official 12-skill collection.
Best for: Teams deploying models for production inference at scale. Works with: Claude Code, Cursor, Codex CLI.
What the Agent Learns
Create Endpoint
from together import Together
client = Together()
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}")Hardware Options
| GPU | VRAM | Best For |
|---|---|---|
| H100 80GB | 80GB | Large models, high throughput |
| H200 | 141GB | Largest models |
| A100 80GB | 80GB | Cost-effective |
Endpoint Management
# Scale
client.endpoints.update(endpoint.id, min_replicas=2)
# Monitor
status = client.endpoints.retrieve(endpoint.id)
# Delete
client.endpoints.delete(endpoint.id)FAQ
Q: How is pricing different from serverless? A: Dedicated endpoints charge per GPU-hour, not per token. Better economics at sustained high volume.