TinyViT-5M (timm/tinyvit5m224.distin22kftin1k, Apache-2.0) quantized to INT8 with Kenosis — 128-image calibration, no retraining. 80.53% top-1 from a 9.2 MB single file, on ONNX Runtime or OpenVINO, CPU or GPU, no accelerator required. ImageNet-1K validation, 49,872 images (disjoint from the 128 calibration images). Measured on a CPU with AVX-VNNI; on CPUs without VNNI this model's INT8 top-1 sits ~0.9 below FP32 rather than 0.34. Input 1x3x224x224, RGB, /255, ImageNet mean/std. Output logits [1,1000], sorted-synset order. runclassify.py / evalimagenet.py reproduce the demo and table. tinyvit5m224int8kenosis.onnx (9,228,567 B) — SHA-256…