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<title>AIDP Video Forge: GPU-Accelerated Video Processing on Decentralized Compute Networks</title>
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 <h1>AIDP Video Forge</h1>
 <p style="font-size: 1.1rem; color: var(--muted); margin-bottom: 1rem;">GPU-Accelerated Video Processing on Decentralized Compute Networks</p>
 <p class="meta">Matthew Karsten &middot; <a href="https://github.com/ExpertVagabond">Purple Squirrel Networks</a> &middot; February 2026</p>
 <div style="margin-top: 1rem;">
 <span class="badge">gpu-acceleration</span>
 <span class="badge">nvenc</span>
 <span class="badge">cuda</span>
 <span class="badge">video-processing</span>
 </div>
</header>
<h2>Abstract</h2>
<p>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 <strong>10-20x faster video encoding</strong> compared to CPU-based methods. Through intelligent job orchestration and distributed batch processing, we achieve <strong>40-60% cost reduction</strong> versus centralized cloud GPU services while maintaining professional-grade video quality.</p>
<h2>Key Results</h2>
<div class="results-grid">
 <div class="result-card"><div class="value">16x</div><div class="label">Faster than CPU</div></div>
 <div class="result-card"><div class="value">58%</div><div class="label">Cost Reduction</div></div>
 <div class="result-card"><div class="value">95.8</div><div class="label">VMAF Score</div></div>
 <div class="result-card"><div class="value">37x</div><div class="label">Distributed (5 GPU)</div></div>
</div>
<table>
 <tr><th>Metric</th><th>AIDP Video Forge</th><th>AWS MediaConvert</th><th>Improvement</th></tr>
 <tr><td>Encoding Speed (4K)</td><td>2.8 min</td><td>3.2 min</td><td><strong>16x faster than CPU</strong></td></tr>
 <tr><td>Cost per Hour</td><td>$0.25</td><td>$0.60</td><td><strong>58% cheaper</strong></td></tr>
 <tr><td>Quality (VMAF)</td><td>95.8</td><td>96.0</td><td>Near-identical</td></tr>
 <tr><td>Distributed (5 GPUs)</td><td>1.2 min</td><td>N/A</td><td><strong>37x faster than CPU</strong></td></tr>
</table>
<h2>Architecture</h2>
<pre><code>+----------------------------------------------------------+
| Video Forge |
+----------------------------------------------------------+
| Client (Web UI / CLI) |
| +-- Upload video -&gt; Select processing -&gt; Download |
+----------------------------------------------------------+
| Job Orchestrator |
| +-- Queue jobs -&gt; Assign to AIDP nodes -&gt; Aggregate |
+----------------------------------------------------------+
| AIDP GPU Workers |
| +-- FFmpeg + NVENC + CUDA filters |
+----------------------------------------------------------+</code></pre>
<h2>GPU Acceleration: NVENC vs CPU</h2>
<table>
 <tr><th>Operation</th><th>CPU Method</th><th>GPU Method</th><th>Speedup</th></tr>
 <tr><td>H.264 Encoding</td><td>libx264</td><td>h264_nvenc</td><td><strong>15-20x</strong></td></tr>
 <tr><td>HEVC Encoding</td><td>libx265</td><td>hevc_nvenc</td><td><strong>20-30x</strong></td></tr>
 <tr><td>Scaling</td><td>scale</td><td>scale_cuda</td><td>5-8x</td></tr>
 <tr><td>Deinterlacing</td><td>yadif</td><td>yadif_cuda</td><td>8-10x</td></tr>
 <tr><td>HDR Tone Map</td><td>zscale+tonemap</td><td>tonemap_cuda</td><td>15x</td></tr>
 <tr><td>LUT Application</td><td>lut3d</td><td>CUDA texture</td><td>10x</td></tr>
</table>
<h2>Processing Speed Benchmark</h2>
<table>
 <tr><th>Method</th><th>Time (10-min 4K)</th><th>Real-time Speed</th><th>Speedup</th></tr>
 <tr><td>CPU (libx264)</td><td>45 minutes</td><td>0.22x</td><td>1x baseline</td></tr>
 <tr><td>AWS MediaConvert (T4)</td><td>3.2 minutes</td><td>3.1x</td><td>14x faster</td></tr>
 <tr><td><strong>AIDP Video Forge (RTX 3090)</strong></td><td><strong>2.8 minutes</strong></td><td><strong>3.6x</strong></td><td><strong>16x faster</strong></td></tr>
 <tr><td><strong>Distributed (5 GPUs)</strong></td><td><strong>1.2 minutes</strong></td><td><strong>8.3x</strong></td><td><strong>37x faster</strong></td></tr>
</table>
<h2>Technical Contributions</h2>
<ol style="padding-left: 1.5rem; margin-bottom: 1.5rem;">
 <li style="margin-bottom: 0.5rem;"><strong>Hardware Acceleration</strong>: Full NVENC/CUDA pipeline eliminating CPU bottlenecks</li>
 <li style="margin-bottom: 0.5rem;"><strong>Distributed Processing</strong>: Intelligent job splitting across multiple GPU nodes</li>
 <li style="margin-bottom: 0.5rem;"><strong>Cost Efficiency</strong>: 40-60% reduction vs centralized cloud GPU services</li>
 <li style="margin-bottom: 0.5rem;"><strong>Quality Preservation</strong>: VMAF 95.8 — near-identical to reference encoding</li>
</ol>
<h2>Citation</h2>
<div class="bibtex">@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}
}</div>
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 <p><a href="https://huggingface.co/purplesquirrelnetworks/aidp-video-forge-paper">View on Hugging Face</a> &middot; <a href="https://huggingface.co/purplesquirrelnetworks/aidp-neural-cloud-paper">Companion: AIDP Neural Cloud</a></p>
 <p style="margin-top: 0.5rem;">Built by <a href="https://github.com/ExpertVagabond">Purple Squirrel Networks</a></p>
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