Universal on‑device AI SDKs and frameworks streamline running vision and multimodal models directly on phones, cameras, and embedded devices. This topic covers established mobile runtimes (Core ML, TFLite), lightweight LLM runtimes (llama.cpp), and universal/bridge SDKs (QVAC) that focus on model portability, hardware‑aware compilation, quantization, and efficient inference across NPUs, GPUs and CPUs. Relevance in 2026: broad NPU adoption, tighter privacy and latency requirements, and demand for offline or hybrid deployments have moved substantial inference workloads to the edge. Simultaneously, model vendors and platform providers—enterprise LLM services like Cohere and Mistral—are offering private, customizable models and tooling that teams increasingly want to run on local hardware. Lightweight runtimes such as llama.cpp enable local LLM use, while Core ML and TFLite remain the primary optimized backends for mobile and embedded vision. Universal SDKs (e.g., QVAC‑style offerings) aim to reduce friction by unifying conversion, quantization, and runtime selection across these ecosystems. Key tools and roles: Core ML and TFLite provide platform‑native model execution and optimizations; llama.cpp enables compact LLMs on-device; QVAC‑style SDKs act as orchestration layers for conversion, hardware abstraction, and performance tuning. No‑code/low‑code platforms (Anakin.ai, StackAI) and enterprise toolchains (Cohere, Mistral, FirstQuadrant) connect model sourcing, governance, and application workflows to on‑device deployment pipelines. Practitioners should evaluate conversion fidelity, quantization support, hardware backends, and governance features when choosing a stack—prioritizing reproducible benchmarks, maintainability, and privacy/latency tradeoffs for edge vision applications.