purplesquirrelnetworks/aidp-video-forge-paper

AIDP Video Forge: GPU-Accelerated Video Processing on Decentralized Compute Networks

Authors: Matthew Karsten (Purple Squirrel Networks) Date: February 2026 License: MIT

📄 Abstract

We present AIDP Video Forge, a GPU-accelerated video processing system leveraging decentralized compute networks. Our approach utilizes NVIDIA's hardware encoding (NVENC) and CUDA-accelerated filters across distributed GPU nodes to provide 10-20x faster video encoding compared to CPU-based methods. Through intelligent job orchestration and distributed batch processing, we achieve 40-60% cost reduction versus centralized cloud GPU services while maintaining professional-grade video quality.

🎯 Key Results

Metric	AIDP Video Forge	AWS MediaConvert	Improvement
Encoding Speed (4K)	2.8 min (10-min video)	3.2 min	⚡ 16x faster than CPU
Cost per Hour	$0.25	$0.60	💰 58% cheaper
Quality (VMAF)	95.8	96.0	📊 Near-identical
Distributed (5 GPUs)	1.2 min	N/A	🚀 37x faster than CPU

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                   Video Forge                           │
├─────────────────────────────────────────────────────────┤
│  Client (Web UI / CLI)                                  │
│  └── Upload video → Select processing → Download result │
├─────────────────────────────────────────────────────────┤
│  Job Orchestrator                                       │
│  └── Queue jobs → Assign to AIDP nodes → Aggregate     │
├─────────────────────────────────────────────────────────┤
│  AIDP GPU Workers                                       │
│  └── FFmpeg + NVENC + CUDA filters                     │
└─────────────────────────────────────────────────────────┘

🚀 Quick Start

# Install dependencies
pip install aidp-video-forge

# Configure AIDP credentials
export AIDP_API_KEY="your-api-key"
export AIDP_WALLET="your-solana-wallet"

# Process a video on AIDP GPU
python -m aidp_video_forge process \
  --input video.mp4 \
  --preset cinematic \
  --output processed.mp4

# Batch process folder
python -m aidp_video_forge batch \
  --input ./videos \
  --lut Cinematic_Teal_Orange.cube

📊 GPU Acceleration Comparison

NVENC vs. CPU Encoding

Operation	CPU Method	GPU Method (NVENC)	Speedup
H.264 Encoding	libx264	h264_nvenc	15-20x
HEVC Encoding	libx265	hevc_nvenc	20-30x
Scaling	scale	scale_cuda	5-8x
Deinterlacing	yadif	yadif_cuda	8-10x
HDR Tone Map	zscale+tonemap	tonemap_cuda	15x
LUT Application	lut3d	CUDA texture	10x

Processing Speed Benchmark

Method	Time (10-min 4K video)	Real-time Speed	Speedup
CPU (libx264)	45 minutes	0.22x	1x baseline
AWS MediaConvert (T4)	3.2 minutes	3.1x	14x faster
AIDP Video Forge (RTX 3090)	2.8 minutes	3.6x	16x faster
Distributed (5 GPUs)	1.2 minutes	8.3x	37x faster

💰 Cost Analysis

Hourly Processing Costs

Provider	GPU Type	Cost/Hour	Minutes Processed/Hour	Cost per Minute
AWS MediaConvert	T4	$0.60	~20 min	$0.030
GCP Transcoder API	T4	$0.55	~18 min	$0.031
AIDP Video Forge	RTX 3090	$0.25	~22 min	$0.011

Annual Savings:

Scenario	Video Volume	AIDP Cost	AWS Cost	Annual Savings
YouTuber	100 hours/month	$66	$180	$1,368/year
Production Studio	500 hours/month	$330	$900	$6,840/year
Streaming Platform	2000 hours/month	$1,320	$3,600	$27,360/year

🎨 Processing Presets

1. Cinematic Preset

• Color Grading: Teal-orange LUT application (CUDA-accelerated)
• Effects: Film grain overlay, letterbox aspect ratio
• Output: ProRes 422 HQ or H.264 high bitrate
• Performance: 12-15x real-time for 4K

2. Broadcast Preset

• Color Space: Rec709 compliance with safe color limiting
• Audio: Loudness normalization (-23 LUFS, EBU R128)
• Output: H.264 Level 4.1 with AAC audio
• Performance: 18-22x real-time for 1080p

3. Social Media Preset

• Aspect Ratio: Automatic vertical crop (9:16)
• Captions: AI-generated subtitles (burn-in or sidecar)
• Thumbnail: Auto-extract keyframe at optimal position
• Performance: 25-30x real-time for 1080p

4. HDR Preset

• Conversion: SDR → HDR10 with GPU tone mapping
• Color Space: Rec.2020 with PQ transfer function
• Metadata: HDR10 static metadata injection
• Performance: 8-12x real-time for 4K HDR

🔬 Technical Contributions

1. GPU Acceleration: 10-20x faster encoding with NVENC (H.264/HEVC)
2. CUDA Optimization: Real-time color grading and HDR processing
3. Distributed Batch Processing: Parallel job execution across GPU nodes
4. Cost Efficiency: 40-60% reduction in processing costs
5. Professional Quality: Maintains broadcast-grade video output (VMAF 95+)

🌍 Supported Formats

Input

• MP4, MOV, MKV, AVI
• ProRes, DNxHD, H.264, HEVC
• Up to 8K resolution

Output

• H.264 (NVENC h264_nvenc)
• HEVC/H.265 (NVENC hevc_nvenc)
• ProRes 422/422 HQ (CPU fallback)
• Multiple audio codecs (AAC, MP3, PCM)

📖 Full Paper

Read the complete research paper: aidp-video-forge-paper.md

🔗 Links

• Live Demo: https://video-forge.aidp.store
• AIDP Marketplace: https://aidp.store/marketplace/video-forge
• Code Repository: https://github.com/purplesquirrelnetworks/aidp-video-forge
• Twitter/X: https://x.com/purplesquirrelnetworks

📚 Citation

@article{karsten2026videoforge,
  title={AIDP Video Forge: GPU-Accelerated Video Processing on Decentralized Compute Networks},
  author={Karsten, Matthew},
  journal={Purple Squirrel Networks Technical Report},
  year={2026},
  url={https://huggingface.co/purplesquirrelnetworks/aidp-video-forge-paper}
}

🙏 Acknowledgments

We thank the AIDP community for providing decentralized GPU infrastructure and the FFmpeg team for CUDA/NVENC filter implementations.

📄 License

MIT License - See LICENSE for details

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Keywords: GPU Acceleration, Video Processing, NVENC, CUDA, Decentralized Infrastructure, Cost Optimization