In 2026, computer vision (CV) workloads account for 62% of all production ML inference spend, up from 41% in 2023, yet 41% of teams report wasted GPU cycles on framework overhead according to a 2026 MLPerf survey. PyTorch 2.5.0 and TensorFlow 2.17.0 both shipped 2026-specific CV optimizations—including fused CV kernels, dynamic shape improvements, and distributed training cost reductions—but only one delivers sub-10ms ImageNet inference on commodity NVIDIA A100 80GB GPUs. This article breaks down every claim with benchmark-backed numbers, runnable code, and real-world case studies to help you choose the right framework for your 2026 CV workloads. 📡 Hacker News Top Stories Right Now Ghostty is leaving GitHub (1531 points) ChatGPT serves ads.…