gpu-provisioner

Provisions cost-efficient GPU instances for LLM training across multiple cloud providers.

Updated Feb 17, 2026

Search Tags:#cost optimization #gpu #provisioning #runpod #cloud compute #llm training #vast.ai

Ready to run

npx skills add https://github.com/Rachasumanth/text2llm001 --skill gpu-provisioner

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gpu-provisioner
Source: https://github.com/Rachasumanth/text2llm001/tree/main/workspace/skills/gpu-provisioner
Command: npx skills add https://github.com/Rachasumanth/text2llm001 --skill gpu-provisioner

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users find and manage cost-effective GPU instances for training large language models, ensuring strict confirmation gates for all spending.

Core Features & Use Cases

GPU Instance Sourcing: Queries multiple cloud providers (RunPod, Vast.ai, Lambda, Kaggle) for suitable GPU hardware.
Cost Optimization: Ranks instances based on total expected run cost, VRAM fit, throughput, and reliability, not just hourly rates.
Spend Control: Implements mandatory user approval for provisioning and alerts for cost overruns.
Use Case: A researcher needs to train a new LLM and wants to find the most economical way to secure the necessary GPU compute, ensuring they don't overspend.

Quick Start

Use the gpu-provisioner skill to find the cheapest viable GPU instance for training a 7B parameter model with a batch size of 16 and a sequence length of 2048.

Frequently Asked Questions about gpu-provisioner

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

FAQPage Schema

How do I find cost-efficient GPU instances for LLM training across cloud providers?▼

To find cost-efficient GPU instances for LLM training, you can query multiple cloud providers like RunPod and Vast.ai to rank hardware based on total expected run cost, VRAM fit, and throughput rather than just hourly rates.

What is the best way to provision cloud compute on RunPod without overspending?▼

The best way to provision cloud compute on RunPod without overspending is to use mandatory spend confirmation gates that require user approval before provisioning, alongside auto-termination safety features to prevent cost overruns.

Does gpu-provisioner support Vast.ai and Lambda for large language model training?▼

Yes, gpu-provisioner supports Vast.ai and Lambda for large language model training, querying these platforms alongside RunPod and Kaggle to source suitable GPU hardware based on VRAM and reliability.

How do I calculate VRAM requirements for a 7B parameter model with batch size 16?▼

To calculate VRAM requirements for a 7B parameter model with batch size 16 and sequence length 2048, the provisioning tool performs preflight validation to match your specific training configuration against available GPU hardware profiles.

Why does GPU cost optimization rank instances by total expected run cost instead of hourly rates?▼

GPU cost optimization ranks instances by total expected run cost instead of hourly rates because throughput and VRAM fit heavily influence actual training time, meaning a higher hourly rate may yield a lower total cost if the hardware processes data faster.

Do I need specific environment variables for API access to provision GPU instances?▼

Yes, you need specific environment variables for API access to provision GPU instances, as the system requires these credentials to perform preflight validation and interact with RunPod, Vast.ai, Lambda, and Kaggle platforms.