This topic covers the hardware and accelerator platforms powering on‑device and edge AI vision: specialized NPUs and VPUs for low‑power inference, FPGAs for customizable pipelines, neuromorphic chips such as BrainChip’s Akida for event‑driven processing, and alternatives to NVIDIA GPUs for latency‑sensitive or privacy‑constrained deployments. Its relevance has grown as organizations push model execution out of the cloud to meet real‑time requirements, reduce bandwidth and cost, and address data‑sovereignty and energy constraints. Practical deployments combine three layers: efficient models, orchestration, and application tooling. Example capabilities from current toolkits include Archetype AI’s Newton — a “Large Behavior Model” designed for real‑time multimodal sensor fusion and deployable on edge/on‑prem hardware; Mistral AI’s efficiency‑focused foundation models and production platform for constrained environments; Run:ai (NVIDIA Run:ai) for pooling and orchestrating GPU resources across on‑prem and cloud; Anakin.ai’s no‑code apps for rapidly building inference pipelines and vision workflows; and IBM watsonx Assistant for enterprise virtual agents and orchestrated on‑prem automation. Trends to note: model compression and architecture co‑design for accelerators, heterogeneous stacks that mix NPUs/VPUs/FPGAs/GPUs, and orchestration layers that span device, edge server, and cloud. For vision use cases, on‑device inference reduces latency and network exposure, while neuromorphic and low‑precision accelerators offer step‑changes in power efficiency for event‑based cameras and continuous monitoring. Selecting a platform now means evaluating model compatibility, runtime toolchains, orchestration needs, and privacy/operational constraints — not just raw FLOPS — to align hardware choice with real‑world edge vision requirements.