GPU Buying Guide for AI Art

Choosing the right GPU is crucial before starting with AI art software like ComfyUI. This guide will help you understand different GPU options and make the best choice for your needs. Note: This guide was written in November 2024. GPU prices and performance metrics may vary, please use this as a reference only.

GPU Architecture and Performance

NVIDIA GPU architectures and their AI performance characteristics:

• 40 Series (Ada): Supports FP16, BF16, FP8 - Best Performance
• 30 Series (Ampere): Supports FP16, BF16 - Excellent Performance
• 20 Series (Turing): Supports FP16 - Good Performance
• 10 Series (Pascal) & older: FP32 only - Not Recommended

Note: While older architectures can run FP16 models, they lack hardware acceleration support, resulting in significantly slower performance. Don’t be misled by large VRAM in Pascal workstation cards.

GPU Performance Comparison

Platform Support

Windows Platform (S-Tier)

• Rating: ★★★★★
• Supported GPUs: All NVIDIA series, Intel Arc
• Features:
  • Native PyTorch support
  • Excellent driver support
  • Easy setup
  • Complete software ecosystem

Linux Platform (B-Tier)

• Rating: ★★★★
• Supported GPUs:
  • All NVIDIA series (recommended)
  • AMD ROCm supported models
• Features:
  • Slightly better NVIDIA performance than Windows
  • AMD requires ROCm support
  • Lacks optimized torch.nn.functional.scaled_dot_product_attention

MacOS Platform (C-Tier)

• Rating: ★★★
• Supported: M1/M2/M3 series chips
• Features:
  • Official PyTorch support
  • OS updates may affect compatibility
  • Average performance

AMD Windows Platform (D-Tier)

• Rating: ★★
• Features:
  • Requires PyTorch DirectML or custom ZLUDA build
  • Suboptimal user experience
  • Awaiting ROCm support

Use Case Recommendations

1. Hobbyist

• Budget: $400-600
• Recommended:
  • RTX 3070 8GB
  • RTX 3060 12GB
• Suitable for:
  • Up to 50 images daily
  • 512x512 to 768x768 resolution
  • Basic model usage

2. Semi-Professional

• Budget: $600-1000
• Recommended:
  • RTX 3080 10/12GB
  • RTX 3090 24GB
• Suitable for:
  • 100-300 images daily
  • Up to 1024x1024 resolution
  • Multiple model usage

3. Professional

• Budget: $1000+
• Recommended:
  • RTX 4090 24GB
  • RTX 4080 16GB
• Suitable for:
  • Batch generation
  • High-res (2k-4k)
  • Multiple loaded models

Model VRAM Requirements

Specific Application Scenario Configuration Suggestions

Flux Model Use Case

• Entry Configuration (FLUX.1 Schnell FP8/Schnell):

  • GPU: RTX 3060 8GB/12GB
  • Suitable: Personal creation and local deployment
  • Features:
    • FP8 version supports low VRAM usage
    • Commercial license available
    • Suitable for personal creators

• Research Configuration (FLUX.1 Dev):

  • GPU: RTX 3090/4090
  • Suitable: Research and testing
  • Features:
    • Full version requires 16GB+ VRAM
    • Only for research purposes
    • Supports more advanced features

Flux Model Performance Optimization Suggestions

1. VRAM Optimization:

   • Prioritize using FP8 quantized version to save VRAM
   • Batch size adjusted according to VRAM capacity
   • Use CUDA acceleration for optimal performance

2. System Requirements:

   • CPU: Recommended 12th Gen i5 or higher
   • System Memory: Minimum 16GB, recommended 32GB
   • Storage: Recommended NVMe SSD
   • CUDA Driver: Keep up-to-date

3. Usage Suggestions:

   • Choose Schnell version for commercial scenarios
   • Choose Dev version for research scenarios
   • Lower configurations prioritize FP8 quantized version

AI Video Generation Scenario

• Basic Configuration (AnimateDiff):
  • Minimum VRAM: 12GB
  • Recommended GPU: RTX 3060 12GB or higher
  • Suitable: Simple animation generation
• Advanced Configuration (SVD/MovieGen):
  • Minimum VRAM: 16GB
  • Recommended GPU: RTX 4080/3090
  • Suitable: High-quality video generation
• Professional Configuration (Multi-model collaboration):
  • VRAM requirements: 24GB+
  • Recommended GPU: RTX 4090
  • Suitable: Professional video production

Performance Improvement Suggestions

1. System Optimization:

   • Use SSD for storing model files
   • Maintain sufficient system memory (recommended 32GB+)
   • Keep the GPU driver up-to-date

2. Usage Tips:

   • Use appropriate batch sizes for batch generation
   • Properly set VAE decoder batch sizes
   • Appropriately use xformers for optimization

3. Flux Model Optimization:

   • Schnell version suitable for VRAM-limited scenarios
   • Dev version recommended for use with LoRA
   • Pro version used via API for more stable performance
   • Structure control models loaded on demand to save VRAM

4. Video Generation Optimization:

   • Properly set keyframe numbers
   • Use smaller resolutions for testing
   • Pay attention to temporary file storage space

Notes

1. VRAM Selection:

   • 8GB is the current minimum practical standard
   • 12GB is a comfortable mid-range choice
   • 24GB is suitable for professional use

2. Purchase Suggestions:

   • Prioritize new GPUs
   • Used GPUs should be cautious of mining card risks
   • Pay attention to cooling design

3. System Configuration:

   • CPU recommended 12th Gen i5 or higher
   • Minimum 16GB of RAM, recommended 32GB
   • Power supply should have a 30% reserve

4. Special Usage Notes:

   • FLUX.1 dev recommends 24GB VRAM for optimal experience
   • Additional VRAM should be reserved for control networks
   • API services can reduce local hardware requirements

5. Architecture Selection Suggestions:

   • Prefer 30/40 series GPUs for optimal performance
   • 20 series as a budget option is acceptable
   • Avoid selecting 10 series and older GPUs
   • Workstation GPUs large VRAM does not necessarily mean good performance

6. Platform Selection Suggestions:

   • Windows + NVIDIA is the best combination
   • Linux platform is suitable for advanced users
   • Avoid using AMD GPUs on Windows