GPU Cloud CLI — Launch & Manage GPUs from Your Shell

Install & authenticate

setup, once

pip install powergpu
export POWERGPU_API_KEY=pg_live_…   # console → API keys
powergpu whoami
# account ok · balance $41.22 · 2 instances running

The key can also live in ~/.config/powergpu/config.toml — the CLI reads the environment first, then the file. Create keys in the console (key docs).

Find capacity

# the price sheet, in your terminal
powergpu gpus --sort price

# machines matching a shape
powergpu offers --gpu rtx-4090 --num 2 --region eu-west-1
# ID          GPU        ×  REGION      vCPU  RAM    DISK   $/HR
# m-8b02c1f3  RTX 4090   2  eu-west-1   16    64GB   1TB    0.524

Launch & manage

lifecycle

powergpu launch --gpu rtx-4090 --template comfyui --disk 60
# ✓ instance i-b81f02aa running (28.4s)
# ✓ https://i-b81f02aa.powergpu.io:8188  (comfyui)

powergpu list                     # your fleet, states, burn/hr
powergpu logs i-b81f02aa -f       # stream container logs
powergpu stop i-b81f02aa          # GPU billing ends this second
powergpu start i-b81f02aa
powergpu destroy i-b81f02aa --yes

Volumes & snapshots

powergpu volume create --name models --size 500 --region eu-west-1
powergpu launch --gpu l40s --template vllm --volume models:/models:ro
powergpu snapshot create i-b81f02aa --name golden

Scripting patterns

Every command takes --json for machine-readable output, and exit codes follow convention (0 ok, 1 API error, 2 usage):

ephemeral GPU jobs

# deploy, wait, run, destroy — a disposable GPU in a Makefile
ID=$(powergpu launch --gpu rtx-4090 --template pytorch --json | jq -r .id)
powergpu wait "" --state running
powergpu exec "" -- python train.py
powergpu destroy "" --yes

Shell completion

powergpu completion bash >> ~/.bashrc     # zsh & fish too

Building in Python instead? The SDK page shows the same flows as typed calls.

The powergpu CLI: the console, without the browser