Breaking the GPU Barrier: A 19x Speedup Optimization Framework for YOLO26-CLIP Video Indexing on 4-Core Cloud Infrastructure

This paper presents a novel four-phase optimization framework that breaks the GPU dependency barrier for YOLO26-CLIP video indexing pipelines deployed on CPU-only cloud infrastructure. Our methodology achieves a 19.2x speedup (from 480 seconds to 25 seconds for 90-second Full HD videos) on Azure Container Apps with only 4 vCPUs and 8GB RAM. The framework combines:(1) Adaptive temporal sampling with scene change detection, reducing frame processing by 98.4%(2) OpenVINO and ONNX Runtime acceleration, achieving 2.47x inference speedup(3) PyTorch inference_mode optimization with batch processing, providing 10.5x embedding throughput improvement(4) Parallel I/O operations with ThreadPoolExecutor, increasing CPU utilization from 45% to 92% Key results:- 19.2x speedup over baseline implementation- 87% cost reduction ($2,300 → $300/month)- 99.5% accuracy retention compared to GPU baseline- First benchmark of YOLO26 for video indexing applications This work enables practitioners to deploy production-grade semantic video search systems on cost-effective edge infrastructure without GPU dependency.