vast-gpu

Provision and manage vast.ai GPU instances for training tasks.

1|1|Updated May 19, 2026

Search Tags:#cost optimization #ml training #vast.ai #experiment automation #gpu provisioning #ssh orchestration

Ready to run

npx skills add https://github.com/zhuyingqin/ARIS-WEB --skill vast-gpu-zhuyingqin

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vast-gpu
Source: https://github.com/zhuyingqin/ARIS-WEB/tree/main/crates/runtime/assets/skills/vast-gpu
Command: npx skills add https://github.com/zhuyingqin/ARIS-WEB --skill vast-gpu-zhuyingqin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) components.

What problem does it solve?

This Skill solves the problem of getting the right GPU hardware for training without guessing models, VRAM, or instance counts.

Core Features & Use Cases

Task-driven GPU selection: Analyzes the training task (plan, scripts, or user description) to estimate VRAM, disk, CUDA needs, and runtime.
Cost-optimized offer matching: Searches vast.ai offers across tiers and ranks options by estimated total cost (not just $/hr).
Full lifecycle automation: Provisions an instance, installs common dependencies, syncs code, verifies GPU availability, and destroys the instance to stop billing.

Quick Start

Use the vast-gpu skill to rent a suitable GPU for your training by saying: /run-experiment "Fine-tune a LLaMA-7B model on a 10k sample dataset for about 2 hours with auto_destroy enabled, and use GPU: vast."

Frequently Asked Questions about vast-gpu

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automatically rent a GPU on vast.ai for ML training without manually picking hardware?▼

You can automatically rent vast.ai GPUs by specifying your training goals, dataset size, and runtime constraints. The skill analyzes these task requirements to estimate VRAM and CUDA needs, searches cost-optimized offers, provisions the instance, and connects via SSH without manual hardware selection.

How does cost optimization work when provisioning on-demand GPU instances?▼

Cost optimization for GPU provisioning works by searching vast.ai offers across multiple tiers and ranking them by estimated total cost. Instead of just looking at the hourly rate, it calculates the projected total expense based on your specified runtime to find the cheapest valid instance.

Can I sync my project code and install dependencies automatically on a rented vast.ai instance?▼

Yes, you can sync project code and install dependencies automatically on a rented vast.ai instance. The automation handles full lifecycle provisioning, connects via SSH orchestration, installs required packages, verifies GPU availability, and syncs your local codebase before running experiments.

What is the best way to stop billing on vast.ai when an ML training experiment finishes?▼

The best way to stop billing on vast.ai is to enable auto_destroy in your experiment configuration. The skill reliably tears down instances after training completes, tracking all active instances in a vast-instances.json file to ensure no compute resources are left accruing charges.

Do I need to manually specify VRAM or GPU models for task-driven experiment automation?▼

No, you do not need to manually specify VRAM or GPU models for task-driven experiment automation. You simply provide the training plan, scripts, or description, and the system estimates the exact VRAM, disk, and CUDA requirements needed to execute the job successfully.