GPU Value Analysis for Machine Learning
Interactive analysis of GPU price/performance for ML workloads with changeable axes and CUDA support visualization
GPU Value Analysis for ML Workloads
Interactive scatter plot showing price vs performance/VRAM, colored by CUDA support level
CUDA Support Levels:
Bright Green: Latest CUDA (9.0+) - RTX 50/40 Series
Dark Green: CUDA 8.6 - RTX 30/A Series
Yellow: Good CUDA (6.0-7.9) - RTX 20/GTX Series
Orange: Limited CUDA (3.5-5.9) - Maxwell
Red: Deprecated CUDA (<3.5) - Kepler
Blue: AMD ROCm Support
VRAM (GB) vs Used Price ($)
💰 Best VRAM Value
🚀 Best Performance Value
💾 Cheapest High-VRAM (16GB+)
💡 Key ML Insights
- H100: Ultimate AI performance king (200) with 80GB VRAM - enterprise-grade
- Tesla M10: Amazing 32GB VRAM for $165 (0.194 GB/$) - virtualization focus but usable
- Tesla K80: Still unbeatable 24GB VRAM for $100 (0.24 GB/$) - compatibility concerns
- RTX 5090: Consumer performance king with 32GB VRAM for large models
- Professional 48GB Tier: RTX 6000 Ada, A6000, 5880 Ada offer massive VRAM
- Data Center vs Workstation: A100/H100 for training, RTX series for development
- VRAM Sweet Spot: 16-24GB covers most models, 48GB+ for cutting-edge research
- Budget Champions: Old Tesla cards offer incredible VRAM/$ despite age
⚠️ Important Compatibility Notes
Red-tier GPUs like Tesla K80 offer exceptional VRAM value but may have compatibility issues with modern ML frameworks. Yellow and Green tier cards provide the best balance of compatibility and performance for most ML workloads. AMD cards require ROCm setup and have limited framework support compared to CUDA.