GPU CLI

Run any code on cloud GPUs with a single command. Just prefix your normal commands with gpu run.

python train.py # local
gpu run python train.py # remote GPU

Features

• Simple - Prefix commands with gpu run, that's it
• Fast - Connection pooling, delta sync, real-time output streaming
• Cost-efficient - Auto-stops pods when idle (save 60-98% on GPU costs)
• Multi-cloud - RunPod, Vast.ai, local Docker
• Secure - Zero-trust encryption on supported providers
• Teams — Organizations with pooled sessions, sub-accounts, and CI/CD service tokens (Team & Enterprise)

Quick Start

# 1. Install GPU CLI
curl -fsSL https://gpu-cli.sh/install.sh | sh
# 2. Run your code on a remote GPU
gpu run python train.py

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Claude Code Plugin

This repo includes a Claude Code plugin that supercharges GPU CLI with AI assistance. Describe what you want in plain English, and Claude generates complete, runnable GPU workflows.

What's Included

Skills (Automatic AI Capabilities)

Skill	Description
gpu-workflow-creator	Transform natural language into complete GPU projects
gpu-ml-trainer	LLM fine-tuning, LoRA training, classifier training
gpu-inference-server	Set up vLLM, TGI, or custom inference APIs
gpu-media-processor	Whisper transcription, voice cloning, video generation
gpu-cost-optimizer	GPU selection advice and cost optimization
gpu-debugger	Debug failed runs, OOM errors, connectivity issues

Slash Commands

Command	Description
/gpu-cli:gpu-create	Create a complete project from a description
/gpu-cli:gpu-optimize	Analyze and optimize your gpu.jsonc
/gpu-cli:gpu-debug	Debug a failed GPU run
/gpu-cli:gpu-quick	Quick-start common workflows

Example Conversations

Create a LoRA training project:

You: I want to train a LoRA on photos of my dog so I can generate images of it
Claude: [Generates complete project with gpu.jsonc, train.py, requirements.txt, README.md]

Set up a private LLM API:

You: Set up Llama 3.1 70B as a private ChatGPT-like API
Claude: [Generates vLLM server config with OpenAI-compatible endpoints]

Debug an error:

You: /gpu-cli:gpu-debug CUDA out of memory when running FLUX
Claude: [Analyzes error, suggests reducing batch size or upgrading to A100]

Optimize costs:

You: /gpu-cli:gpu-optimize
Claude: [Reviews gpu.jsonc, suggests RTX 4090 instead of A100 for your workload, saving 75%]

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Templates

Ready-to-use templates for common AI/ML workflows:

Template	GPU	Description
Ollama Models	RTX 4090	Run LLMs with Ollama - includes Web UI and OpenAI-compatible API
vLLM Models	RTX 4090	High-performance LLM inference with vLLM
Background Removal	RTX 4090	Remove backgrounds from images using AI
CrewAI Stock Analysis	RTX 4090	Multi-agent stock analysis with CrewAI + Ollama
HuggingFace Gradio	RTX 4090	Run HuggingFace models with Gradio UI
Qwen Image Edit	RTX 4090	Edit images using Qwen vision model

Common Commands

# Run a command on remote GPU
gpu run python script.py
# Run a server with port forwarding
gpu run -p 8188:8188 python server.py --listen 0.0.0.0
# Open a shell on the remote pod
gpu shell
# View running pods
gpu pods list
# Stop a pod
gpu stop
# Interactive dashboard
gpu dashboard

Team Management

# Create an organization
gpu org create "My Team"# Switch to org context
gpu org switch my-team
# Invite a teammate
gpu org invite alice@example.com --role admin
# Create a CI/CD service account
gpu org service-account create --name "github-actions"

Configuration

Create a gpu.jsonc in your project:

{
"$schema": "https://gpu-cli.sh/schema/v1/gpu.json",
"project_id": "my-project",
"provider": "runpod",
// Sync outputs back to local machine"outputs": ["output/", "models/"],
// GPU selection"gpu_type": "RTX 4090",
"min_vram": 24,
// Optional: Pre-download models"download": [
{ "strategy": "hf", "source": "black-forest-labs/FLUX.1-dev", "allow": "*.safetensors" }
],
"environment": {
"base_image": "runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04"
}
}

Network Volumes (Recommended)

For faster startup and persistent model storage, use RunPod Network Volumes. See the Network Volumes Guide for setup instructions.

GPU Options

GPU	VRAM	Best For	Cost/hr
RTX 4090	24GB	Image generation, LoRA training	~$0.44
RTX 4080	16GB	SDXL, most workflows	~$0.35
A100 40GB	40GB	70B models, video generation	~$1.29
A100 80GB	80GB	70B+ models, large batch	~$1.79
H100 80GB	80GB	Maximum performance	~$3.99

Documentation

• Network Volumes Guide - Persistent storage for models
• Organizations Guide - Team billing, sub-accounts, and service tokens
• GPU CLI Docs - Full documentation

License

MIT