Single-Thread JPEG Decoder Benchmarks Mis-Evaluate ML Data Loaders

JPEG decode is routine ML infrastructure, but Python decoder choices are often justified by single-process, single-thread microbenchmarks. We audit this evaluation assumption with thirteen Python-accessible JPEG decode paths on five matched 16 vCPU Google Cloud CPUs: Intel Emerald Rapids, AMD Zen 4, AMD Zen 5, ARM Neoverse V2, and ARM Neoverse N1. ImageNet validation is the workload, not a new dataset contribution: each run decodes the full 50,000-image split from memory and reports single-thread throughput for all decoders, PyTorch $\mathtt{\text{DataLoader}}$ throughput for eligible decoders at worker counts $\{0,2,4,8\}$, and decoder skip behavior. The evaluation protocol changes the supported conclusion. On Neoverse V2, $\mathtt{\text{imageio}}$ is ninth in single-thread throughput yet lands in the top DataLoader tier with $\mathtt{\text{torchvision}}$; on Zen 4, $\mathtt{\text{torchvision}}$ rises from seventh single-thread to the top measured DataLoader tier; on Neoverse N1, $\mathtt{\text{imagecodecs}}$ is the single-thread leader but fifth at peak DataLoader throughput. We also find that worker-count conclusions differ between Zen 4 and Zen 5, TensorFlow has a large single-thread ARM penalty, and strict native JPEG decoders/wrappers reject the same rare ImageNet JPEG. For PyTorch DataLoader workloads, $\mathtt{\text{torchvision}}$ and $\mathtt{\text{simplejpeg}}$ form the strongest measured zero-skip tier: $\mathtt{\text{torchvision}}$ has the highest mean normalized throughput, while $\mathtt{\text{simplejpeg}}$ has the highest minimum. OpenCV remains a robust general-purpose fallback above 90% of the platform-local winner on every tested CPU. We release raw JSON, generated tables/figures, and an executable local/cloud benchmark framework.