This topic compares modern edge and mobile AI processors—Intel Core Ultra “Panther Lake,” Apple Silicon (M‑series), and Qualcomm SoCs—through the lens of on‑device models for vision and autonomy. Edge AI Vision Platforms increasingly require low‑latency, privacy‑preserving inference for multimodal sensor fusion, real‑time perception and control, and offline operation. Hardware differences center on CPU/GPU balance, dedicated NPUs and ISP/camera pipelines, memory bandwidth and system integration, and software stacks for model optimization and deployment (e.g., vendor SDKs, Core ML, OpenVINO/compilers, NNAPI-like runtimes). As of 2026, the trend toward on‑device large behavior and multimodal models makes processor selection more consequential: models like Archetype AI’s Newton (a deployable Large Behavior Model for real‑time multimodal sensor fusion) and autonomy stacks such as Shield AI’s Hivemind/EdgeOS demand deterministic low latency, robust NPUs, and reliable vision ISPs. Enterprise and developer needs are addressed by tooling and models from Mistral (open, efficiency‑focused models), no‑code deployment platforms such as Anakin.ai, and orchestration layers like Run:ai when edge clusters include pooled GPUs. Practically, Apple Silicon emphasizes tight hardware‑software integration and efficient neural engines for on‑device ML; Qualcomm offers heterogenous mobile SoCs with Hexagon NPUs and broad Android ecosystem support; Intel’s Panther Lake targets x86 compatibility with expanded on‑chip AI engines and media/ISP improvements for Windows/Linux edge devices. Evaluations should prioritize performance‑per‑watt for target model formats (INT8/FP16/4‑bit), camera/ISP quality for vision tasks, runtime ecosystems, and end‑to‑end deployment—from quantized inference on a device to hybrid orchestration across edge clusters. This comparison helps engineering and procurement teams choose processors that match the model, latency, power, and software requirements of modern edge vision applications.