Question 1

Will auto-stop kill my training?

Accepted Answer

No. Auto-stop only triggers after your command completes. Active GPU usage is never interrupted. Your training runs to completion, then auto-stop kicks in after 5 minutes of idle.

Question 2

How does GPU CLI pricing work?

Accepted Answer

You bring your own GPU provider account and pay the provider directly for compute. When you need hosted model capacity, you buy it per active model per month. Business is available for teams that need stronger governance, automation, and higher operational limits.

Question 3

What happens when I close my laptop?

Accepted Answer

Your job keeps running. Come back anytime and reconnect to see results. Output files sync automatically as they're created.

Question 4

How is this different from runpodctl?

Accepted Answer

runpodctl is great for RunPod infrastructure management. GPU CLI adds a development workflow layer across providers: instant relay connections, automatic file syncing, and auto-stop to save costs.

Question 5

Can you see my code?

Accepted Answer

No. Your code syncs directly to your GPU provider over SSH or secure tunnels. We only see operational metadata such as duration, GPU type, and cost. Never your actual code or outputs.

Question 6

How much does a typical run cost?

Accepted Answer

Typical cost depends on your provider and GPU. On common providers, a 2-hour A100 run is often around $4. If you want hosted model capacity, you buy it per active model per month, and Business is available for advanced operational needs.

Question 7

Is GPU CLI a replacement for OpenAI or Anthropic?

Accepted Answer

GPU CLI is a deployment layer, not an API. You deploy an open-source model to a GPU you control, and GPU CLI exposes an OpenAI-compatible endpoint so your existing client libraries keep working. For agents, chatbots, and RAG workloads, running an open-source model through GPU CLI typically cuts inference cost 60–80% and keeps your data on your instance. Use the calculators at /builders or /developers to see the numbers for your workload.

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