vast-gpu

Vast.ai GPU Management

Manage vast.ai GPU instance: $ARGUMENTS

Overview

Rent cheap, capable GPUs from vast.ai on demand. This skill analyzes the training task to determine GPU requirements, searches for the best-value offers, presents options with estimated total cost, and handles the full lifecycle: rent → setup → run → destroy.

Users do NOT specify GPU models or hardware. They describe the task — the skill figures out what to rent.

Prerequisites: The vastai CLI must be installed (requires Python ≥ 3.10) and authenticated:

pip install vastai
vastai set api-key YOUR_API_KEY

If your system Python is < 3.10, create a virtual environment with Python ≥ 3.10 (e.g., conda create, pyenv, uv venv, etc.) and install vastai there.

SSH public key must be uploaded at https://cloud.vast.ai/manage-keys/ BEFORE creating any instance. Keys are baked into instances at creation time — if you add a key after renting, you must destroy and re-create the instance.

State File

All active vast.ai instances are tracked in vast-instances.json at the project root:

[
  {
    "instance_id": 33799165,
    "offer_id": 25831376,
    "gpu_name": "RTX_3060",
    "num_gpus": 1,
    "dph": 0.0414,
    "ssh_url": "ssh://root@1.208.108.242:58955",
    "ssh_host": "1.208.108.242",
    "ssh_port": 58955,
    "created_at": "2026-03-29T21:12:00Z",
    "status": "running",
    "experiment": "exp01_baseline",
    "estimated_hours": 4.0,
    "estimated_cost": 0.17
  }
]

This file is the source of truth for /run-experiment and /monitor-experiment to connect to vast.ai instances.

Workflow

Action: Provision (default)

Analyze the task, find the best GPU, and present cost-optimized options. This is the main entry point — called directly or automatically by /run-experiment when gpu: vast is set.

Step 1: Analyze Task Requirements

Read available context to determine what the task needs:

1. From the experiment plan (refine-logs/EXPERIMENT_PLAN.md):
2. Compute budget (total GPU-hours)
3. Hardware hints (e.g., "4x RTX 3090")
4. Model architecture and dataset size
5. Run order and per-milestone cost estimates

1. From experiment scripts (if already written):
2. Model size — scan for model class, num_parameters, config files
3. Batch size, sequence length — estimate VRAM from these
4. Dataset — estimate training time from dataset size + epochs
5. Multi-GPU — check for DataParallel, DistributedDataParallel, accelerate, deepspeed

1. From user description (if no plan/scripts exist):
2. Model name/size (e.g., "fine-tune LLaMA-7B", "train ResNet-50")
3. Dataset scale (e.g., "ImageNet", "10k samples")
4. Estimated duration (e.g., "about 2 hours")

Step 2: Determine GPU Requirements

Based on the task analysis, determine:

| Factor | How to estimate | |--------|----------------| | Min VRAM | Model params × 4 bytes (fp32) or × 2 (fp16/bf16) + optimizer states + activations. Rules of thumb: 7B model ≈ 16 GB (fp16), 13B ≈ 28 GB, 70B ≈ 140 GB (needs multi-GPU). ResNet/ViT ≈ 4-8 GB. Add 20% headroom. | | Num GPUs | 1 unless: model doesn't fit in single GPU VRAM, or scripts use DDP/FSDP/DeepSpeed, or plan specifies multi-GPU | | Est. hours | From experiment plan's cost column, or: (dataset_size × epochs) / (throughput × batch_size). Default to user estimate if available. Add 30% buffer for setup + unexpected slowdowns | | Min disk | 20 GB base + model checkpoint size + dataset size. Default: 50 GB | | CUDA version | Match PyTorch version. PyTorch 2.x needs CUDA ≥ 11.8. Default: 12.1 |

Step 3: Search Offers

Search across multiple GPU tiers to find the best value. Always search broadly — do NOT limit to one GPU model:

# Tier 1: Budget GPUs (good for small models, fine-tuning, ablations)
vastai search offers "gpu_ram>=<MIN_VRAM> num_gpus>=<N> reliability>0.95 inet_down>100" -o 'dph+' --storage <DISK> --limit 10

# Tier 2: If VRAM > 24 GB, also search high-VRAM cards specifically
vastai search offers "gpu_ram>=48 num_gpus>=<N> reliability>0.95" -o 'dph+' --storage <DISK> --limit 5

The output is a table with columns: ID, CUDA, N (GPU count), Model, PCIE, cpu_ghz, vCPUs, RAM, Disk, $/hr, DLP (deep learning perf), score, NV Driver, Net_up, Net_down, R (reliability %), Max_Days, mach_id, status, host_id, ports, country.

The first column (ID) is the offer ID needed for vastai create instance.

Step 4: Present Cost-Optimized Options

Present 3 options to the user, ranked by estimated total cost:

Task analysis:
- Model: [model name/size] → estimated VRAM: ~[X] GB
- Training: ~[Y] hours estimated
- Requirements: [N] GPU(s), ≥[X] GB VRAM, ~[Z] GB disk

Recommended options (sorted by estimated total cost):

| # | GPU          | VRAM  | $/hr   | Est. Hours | Est. Total | Reliability | Offer ID  |
|---|-------------|-------|--------|------------|------------|-------------|-----------|
| 1 | RTX 3060    | 12 GB | $0.04  | ~6h        | ~$0.25     | 99.4%       | 25831376  |  ← cheapest
| 2 | RTX 4090    | 24 GB | $0.28  | ~4h        | ~$1.12     | 99.2%       | 6995713   |  ← best value
| 3 | A100 SXM    | 80 GB | $0.95  | ~2h        | ~$1.90     | 99.5%       | 7023456   |  ← fastest

Option 1 is cheapest overall. Option 3 finishes fastest.
Pick a number (or type a different offer ID):

Key presentation rules:

• Always show estimated total cost ($/hr × estimated hours), not just $/hr
• Faster GPUs have shorter estimated hours (scale by relative FLOPS)
• Flag if a cheap option has reliability < 0.97 ("budget pick — 3% chance of interruption")
• If task is small (<1 hour), recommend interruptible pricing for even lower cost
• If no offers meet VRAM requirements, explain why and suggest alternatives (e.g., multi-GPU, quantization)

Relative speed scaling (approximate, for estimating hours across GPU tiers):

| GPU | Relative Speed (FP16) | |-----|-----------------------:| | RTX 3060 | 0.5× | | RTX 3090 | 1.0× | | RTX 4090 | 1.6× | | A5000 | 0.9× | | A6000 | 1.1× | | L40S | 1.5× | | A100 SXM | 2.0× | | H100 SXM | 3.3× |

Use these to scale the base estimated hours across offers.

Action: Rent

Create an instance from a user-selected offer.

Step 1: Create Instance

vastai create instance <OFFER_ID> \
  --image <DOCKER_IMAGE> \
  --disk <DISK_GB> \
  --ssh \
  --direct \
  --onstart-cmd "apt-get update && apt-get install -y git screen rsync"

Default Docker image: pytorch/pytorch:2.1.0-cuda12.1-cudnn8-devel (override via CLAUDE.md image: field if set).

The output looks like:

Started. {'success': True, 'new_contract': 33799165, 'instance_api_key': '...'}

The new_contract value is the instance ID — save this for all subsequent commands.

Step 2: Wait for Instance Ready

Poll instance status every 20 seconds until it's running (typically takes 30-60 seconds, max ~5 minutes):

vastai show instances --raw | python3 -c "
import sys, json
instances = json.load(sys.stdin)
for inst in instances:
    if inst['id'] == <INSTANCE_ID>:
        print(inst['actual_status'])
"

Wait states: loading → running. If stuck in loading for >5 minutes, warn the user — the host may be slow or the image may be large.

Step 3: Get SSH Connection Details

vastai ssh-url <INSTANCE_ID>

This returns a URL in the format: ssh://root@<HOST>:<PORT>

Parse out host and port from this URL. Example:

• Input: ssh://root@1.208.108.242:58955
• Host: 1.208.108.242, Port: 58955

Important: Always use vastai ssh-url to get connection details — do NOT rely on ssh_host/ssh_port from vastai show instances, as those may point to proxy servers that differ from the direct connection endpoint.

Step 4: Verify SSH Connectivity

ssh -o StrictHostKeyChecking=no -o ConnectTimeout=15 -p <PORT> root@<HOST> "nvidia-smi &&