I got tired of spending 30 minutes setting up GPU instances every time I wanted to test a model so I built a CLI that does it in 2 minutes. It's free and open source.
I kept running into the same problem. I want to test a new model, so I open RunPod, check Vast ai, check Lambda, compare prices, spin something up, SSH in, install vLLM, figure out TP settings, pull the model, configure everything. By the time I'm actually running inference I've wasted an hour on ops work.
Then I'd forget to terminate the instance and wake up to a $96 bill. Did that twice before I snapped and built something.
It's called swm. One CLI that talks to 10 GPU clouds. Search available GPUs across all of them sorted by price, spin up an instance, and install vLLM or Ollama with one command. It auto-detects your GPU count and sets tensor parallelism for you.
The part that actually saves the most time though is the workspace sync. Your whole environment lives in S3. When you're done you run swm pod down and it pushes everything, terminates the pod, and you can resume on any provider later with everything exactly where you left it. Models, configs, all of it.
Also built a lifecycle guard that monitors GPU utilization and SSH sessions. If nothing's happening for 30 minutes it saves your workspace and kills the pod automatically. No more overnight bills.
A few things it does:
- swm gpus -g h100 --max-price 3.00 --sort price — compare across RunPod, Vast ai, Lambda, AWS, GCP, Azure, CoreWeave, Vultr, TensorDock, FluidStack
- swm setup install vllm — installs and configures vLLM with correct TP settings automatically
- swm models pull — search HuggingFace and pull to any pod
- swm pod down — push workspace to S3, terminate, resume later on any cloud
- Works with Cursor, Claude Code, Codex, Windsurf any agent that runs shell commands
It's free, open source, Apache 2.0. pipx install swm-gpu
Site: https://swmgpu.com GitHub: https://github.com/swm-gpu/swm
Would love feedback from anyone who rents GPUs regularly. What's annoying about your current workflow that I should build for next?