This topic covers the software runtimes, compiler stacks and hardware accelerators used to run on‑device inference for edge vision and multimodal applications. As of 2025-12-17, demand for real-time, private and resilient inference has pushed development toward compact foundation and behavior models deployable at the edge, deterministic autonomy stacks for mission-critical systems, and orchestration layers that span edge and cloud. Key tool categories include model runtimes and compilers (for example, frameworks that target quantized INT8/INT4, sparsity and operator fusion), device accelerators (NPUs, mobile GPUs, FPGAs, VPUs and DSPs) and orchestration/management platforms that schedule heterogeneous resources. Representative platforms from the provided set illustrate these roles: Archetype AI’s Newton positions a Large Behavior Model for real-time multimodal sensor fusion and reasoning targeted for on‑prem and edge deployments; Shield AI’s Hivemind family (EdgeOS deterministic middleware, Pilot behaviors, Forge factory) demonstrates requirements for deterministic, certifiable autonomy on constrained devices; and Run:ai (NVIDIA Run:ai) shows the need to pool and optimize GPU resources across on‑prem, cloud and hybrid environments for training, validation and heavier inference workloads. Practical design choices balance latency, power, model accuracy and safety/certification. Trends to watch include compiler-driven optimization, hardware‑aware quantization, heterogeneous scheduling between local accelerators and nearby servers, and tighter integration of autonomy middleware for predictable timing. For teams evaluating solutions, focus on supported model formats, toolchain maturity for your accelerator mix, deterministic runtime guarantees, and orchestration paths that match your deployment topology and compliance requirements.