Vast.ai On-Demand GPU Integration

🇨🇳 中文版：VAST_GPU_GUIDE_CN.md Part of the ARIS GPU Server Setup options. Use this when you don't own a GPU server.

ARIS supports renting GPUs on demand from Vast.ai — the cheapest spot-rental marketplace for ML hardware. When you run /run-experiment, ARIS analyzes your training task (model size, dataset, estimated time), searches for the cheapest GPU that fits the workload, and presents options ranked by estimated total cost (not just $/hr). After you pick, it handles everything: rent → setup → run → collect results → destroy.

When to use this vs. `gpu: remote` / `gpu: local`

Option	When	Cost model
`gpu: remote`	You own (or your lab provides) a fixed SSH-accessible server	Sunk cost; ARIS treats it as free
`gpu: local`	You're already on the GPU host	Sunk cost; no SSH overhead
`gpu: vast`	No GPU, or you need bigger hardware than what you own for one experiment	Per-hour rental, auto-billed by Vast.ai

Vast.ai works for one-off ablations, baseline reruns, or scaling up to A100/H100 for a single experiment. Not ideal for week-long training jobs — at that point a dedicated server is cheaper.

Prerequisites

Create a Vast.ai account at https://cloud.vast.ai/ and add billing (credit card or crypto).
Install the vastai CLI (requires Python ≥ 3.10):
```
pip install vastai
```
If your Python is older (check with python --version), use a virtual environment with Python ≥ 3.10 (e.g., conda create, pyenv, uv venv).
Set your API key — get it from https://cloud.vast.ai/cli/:
```
vastai set api-key YOUR_API_KEY
```
Upload your SSH public key at https://cloud.vast.ai/manage-keys/ — this is required before renting any instance (keys are baked in at creation time). If you don't have one:
```
ssh-keygen -t ed25519 -C "your_email@example.com"
cat ~/.ssh/id_ed25519.pub   # copy this to Vast.ai
```

Verify setup — test that search works:

vastai search offers 'gpu_ram>=24 reliability>0.95' -o 'dph+' --limit 3

Tell ARIS to use Vast.ai

Add to your project's CLAUDE.md:

## Vast.ai
- gpu: vast                  # rent on-demand GPU from vast.ai
- auto_destroy: true         # auto-destroy after experiment completes (default)
- max_budget: 5.00           # optional: warn if estimated cost exceeds this

That's it — no GPU model or hardware config needed. ARIS reads your experiment scripts/plan, estimates VRAM and training time, then presents options:

| # | GPU       | VRAM  | $/hr  | Est. Hours | Est. Total | Offer ID |
|---|-----------|-------|-------|------------|------------|----------|
| 1 | RTX 4090  | 24 GB | $0.28 | ~4h        | ~$1.12     | 6995713  |  ← best value
| 2 | A100 SXM  | 80 GB | $0.95 | ~2h        | ~$1.90     | 7023456  |  ← fastest

Pick a number and ARIS handles the rest.

Manual control

For one-off rentals outside the /run-experiment flow, use the dedicated skill:

/vast-gpu                          # interactive — search, pick, rent
/vast-gpu list                     # list your current rented instances
/vast-gpu destroy <instance-id>    # tear down manually

auto_destroy: true will tear instances down after /run-experiment finishes; false leaves them up so you can SSH in and inspect. Always run vastai show instances (or /vast-gpu list) after a session to confirm nothing is silently billing you.

Cost expectations

Typical ARIS workloads with Vast.ai:

Small ablation (single-GPU, 1–4 hours): ~$0.30 – $2 / run on RTX 3090/4090
Bigger baseline rerun (40–80 GB VRAM, multi-hour): ~$2 – $10 / run on A100/H100
Spot-prices fluctuate; vastai search offers reflects live market rates

Set max_budget in CLAUDE.md to get a warning when ARIS's estimate exceeds your comfort zone — it doesn't hard-block, just confirms before renting.

Fallback: no server at all

The review and rewriting skills (/auto-review-loop, /research-review, /paper-writing, /paper-compile) still work without GPU access. Only experiment-related fixes will be skipped (flagged for manual follow-up).

/vast-gpu — direct rental control
/run-experiment — auto-deploy via gpu: vast
/monitor-experiment — collect results from running rentals

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