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

Related Resources

Resource	Link
Model	purple-squirrel-r1
Model (Multichain)	purple-squirrel-r1-multichain
Companion Paper	AIDP Neural Cloud
Live Paper	aidp-video-forge.pages.dev
GitHub	ExpertVagabond

Abstract

We present AIDP Video Forge, a GPU-accelerated video processing system leveraging decentralized compute networks. Our approach utilizes NVIDIA 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      |
+---------------------------------------------------------+
|  Job Orchestrator                                       |
|  +-- Queue jobs -> Assign to AIDP nodes -> Aggregate    |
+---------------------------------------------------------+
|  AIDP GPU Workers                                       |
|  +-- FFmpeg + NVENC + CUDA filters                      |
+---------------------------------------------------------+

Quick Start

pip install aidp-video-forge

export AIDP_API_KEY="your-api-key"
export AIDP_WALLET="your-solana-wallet"

python -m aidp_video_forge process \
  --input video.mp4 \
  --preset cinematic \
  --output processed.mp4

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

Technical Contributions

1. Hardware Acceleration: Full NVENC/CUDA pipeline eliminating CPU bottlenecks
2. Distributed Processing: Intelligent job splitting across multiple GPU nodes
3. Cost Efficiency: 40-60% reduction vs. centralized cloud GPU services
4. Quality Preservation: VMAF 95.8 -- near-identical to reference encoding

Citation

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

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