
camd - Find AMD Hardware on Cloud ☁️
The easiest way to find AMD GPUs and CPUs across cloud providers
Installation • Quick Start • Features • Providers • Hardware • Roadmap
? Overview
camd
(cheapamd) is a command-line tool that helps you find available AMD hardware across cloud providers. With the massive 192GB memory of MI300X GPUs and powerful EPYC CPUs, AMD offers compelling alternatives to NVIDIA hardware.
Why AMD?
- MI300X GPU: 192GB HBM3 memory (2.4x more than H100!)
- High Performance: Excellent compute capabilities
- EPYC CPUs: Best price/performance for CPU workloads
- Availability: Often easier to find than scarce H100s
✨ Features
Current Capabilities (v6.0.0)
- ? Multi-Provider Search: Vultr and RunPod support
- ? AMD GPU Discovery: Find MI300X (192GB) and MI250X (128GB)
- ? AMD CPU Discovery: All EPYC variants (Milan, Rome, Genoa)
- ? Price Comparison: Sort by hourly cost
- ?️ Spot Pricing: 50% discounts on RunPod
- ? Multi-GPU Configs: 1x, 2x, 4x, 8x GPU clusters
- ⚡ Smart Caching: 5-minute cache to reduce API calls
- ? Beautiful CLI: Color-coded output with emojis
- ? Secure: API keys stored locally with 600 permissions
? Installation
# Download the script curl -O https://raw.githubusercontent.com/modelturnedgeek/CheaperNvidia/main/camd.py chmod +x camd.py # Install system-wide sudo cp camd.py /usr/local/bin/camd # Or install for current user mkdir -p ~/.local/bin cp camd.py ~/.local/bin/camd echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc source ~/.bashrc
Requirements
- Python 3.6+
requests
library (pip install requests
)
? Quick Start
1. Setup (One-time)
You'll be guided to get API keys from:
- RunPod: https://www.runpod.io/console/user/settings
- Vultr: https://my.vultr.com/settings/#settingsapi
2. Find AMD Hardware
# List all AMD hardware (GPUs + CPUs) camd list # List only AMD GPUs camd list gpu # List only AMD CPUs camd list cpu
? Sample Output
? camd v6.0.0 - Checking AMD hardware availability...
━━━ AMD GPU Instances ━━━
MI300X: 192GB HBM3 | 5.3TB/s | 1307.4 TFLOPS
? $/hr Provider Model Count VRAM Type Available
─────────────────────────────────────────────────────────────────────────────────
$1.25 RunPod MI300X 1 192GB MI300X-spot ✓
$2.49 RunPod MI300X 1 192GB MI300X-ondemand ✓
$2.50 Vultr MI300X 1 192GB gpu-mi300x-1 ✓
$5.00 Vultr MI300X 2 384GB gpu-mi300x-2 ✓
━━━ AMD CPU Instances ━━━
AMD EPYC processors - Industry leading performance
? $/hr Provider Type vCPUs RAM Category
─────────────────────────────────────────────────────────────────────────────────
$0.01 Vultr vhf-1c-1gb-amd 1 1GB High Frequency AMD
$0.01 Vultr vhp-1c-1gb-amd 1 1GB High Performance AMD
$0.02 Vultr vhf-1c-2gb-amd 1 2GB High Frequency AMD
...
? Supported Providers
Current Providers
Provider | AMD GPUs | AMD CPUs | API Status | Notes |
---|---|---|---|---|
RunPod | ✅ MI300X, MI250X | ❌ | Stable | Best for GPU workloads, spot pricing available |
Vultr | ? Limited | ✅ EPYC | Stable | Excellent CPU selection, some GPU availability |
Provider Details
RunPod
- Strengths: GPU-focused, spot instances (50% off), global availability
- GPUs: MI300X ($2.49/hr), MI250X ($1.99/hr estimated)
- Features: Multi-GPU clusters, persistent storage, Jupyter support
Vultr
- Strengths: Wide CPU selection, hourly billing, 25+ locations
- CPUs: EPYC 7003 (Milan), 7002 (Rome), 9004 (Genoa)
- Types: High Performance (vhp), Optimized Cloud (voc), High Frequency (vhf)
? Use Cases
Perfect for MI300X (192GB)
- 70B+ LLMs: Run Llama-70B on a single GPU!
- RAG Systems: Massive context windows
- Multi-modal AI: Image + text models
- Scientific Computing: Large memory requirements
Perfect for AMD CPUs
- Web Hosting: Better price/performance than Intel
- Databases: High memory bandwidth
- Containers: Excellent multi-threading
- CI/CD: Cost-effective build servers
?️ Advanced Usage
Environment Variables
# API Keys export RUNPOD_API_KEY='your-key' export VULTR_API_KEY='your-key' # Cache timeout (minutes) export CAMD_CACHE_MINUTES=5 # Debug mode export CAMD_DEBUG=1
Configuration File
# Location: ~/.camd/.env
RUNPOD_API_KEY=your_runpod_key
VULTR_API_KEY=your_vultr_key
CAMD_CACHE_MINUTES=5
? Contributing
We welcome contributions! Here's how to add a new provider:
- Create a new provider class inheriting from base
- Implement
get_amd_hardware()
method - Add to provider initialization in
load_config()
- Submit PR with example output
Development Setup
git clone https://github.com/modelturnedgeek/CheaperNvidia cd CheaperNvidia pip install requests # Only dependency python camd.py setup
? Troubleshooting
Common Issues
"No configuration found"
camd setup # Run setup first
"No AMD hardware found"
- Check API keys are valid
- Ensure you have network connectivity
- Try with debug mode:
CAMD_DEBUG=1 camd list
API Rate Limits
- Results are cached for 5 minutes
- Adjust with
CAMD_CACHE_MINUTES
? Resources
? License
MIT License - see LICENSE file
? Acknowledgments
- AMD for making competitive hardware
- Cloud providers offering AMD instances
- The open-source community
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