This topic examines the growing split between edge AI accelerators and on‑device models (including neuromorphic designs like BrainChip’s Akida, NPUs and FPGAs) and cloud‑first accelerators (GPUs, TPUs and large centralized inference fleets). Edge solutions prioritize low latency, reduced bandwidth, privacy and resilience for vision platforms and autonomy systems; cloud‑first approaches favor raw throughput, centralized model orchestration and large‑scale retraining. Relevance in late 2025: regulation, cost pressure on cloud egress, and mature model compression/quantization toolchains have accelerated adoption of on‑device inference for camera‑based vision and mission‑critical autonomy. Decentralized AI infrastructure patterns (local-first model serving, OTA updates, provenance and governance) are now practical for enterprises and defense platforms that need deterministic behavior and data locality. Key tools and roles: BrainChip and other edge accelerators provide event‑driven or highly quantized inference kernels tuned for power‑constrained vision; cloud‑first accelerators (NVIDIA/Google-class stacks) remain essential for large model training and centralized services. Developer and deployment tooling bridges both worlds: Shield AI’s Hivemind/EdgeOS illustrates autonomy stacks that combine deterministic middleware with on‑device behaviors; Tabby, Cline and Tabnine represent local‑first/self‑hosted coding assistants that enable private model serving, auditability and repeatable build pipelines; Windsurf and Warp are agentic developer environments that streamline model iteration and testing for heterogeneous targets. Practical tradeoffs include model fidelity vs power/latency, operational complexity of diverse hardware, and governance for decentralized deployments. Choosing between edge accelerators and cloud‑first approaches depends on application needs (real‑time vision, privacy, resilience) and the maturity of toolchains to compile, verify and update models across distributed infrastructure.