On‑device and local AI inference & optimization toolkits cover the software, runtimes, model formats and orchestration layers that let machine learning models run with low latency, reduced cloud dependence, and stronger data control. This topic spans edge vision platforms that squeeze neural nets onto NPUs and mobile SoCs, decentralized infrastructure that coordinates agentic workloads across devices, and AI data platforms that prepare and surface local context for private inference. As of 2026, demand for local inference is driven by privacy and compliance requirements, latency-sensitive applications, and the maturing capability of smaller, instruction‑tuned models. Key building blocks include model quantization and compilation, lightweight runtime stacks (WASM, ONNX/TFLite/MLC-style backends), on‑device context plumbing, and observability/orchestration for distributed agents. Representative tools illustrate these roles: Stable Code provides edge‑ready, instruction‑tuned code LLMs for private, fast code completion; JetBrains AI Assistant brings context‑aware generation and refactorings inside the IDE; EchoComet focuses on assembling and processing code context entirely on the developer’s device to avoid sending sensitive project data to remote servers; MindStudio offers a no‑/low‑code visual platform to design, test, deploy and operate AI agents with enterprise controls; and Xilos targets enterprise orchestration and visibility for agentic AI across connected services. Together these tool types address complementary needs—model and runtime optimization for performance, developer UX and local context handling for accuracy and privacy, and infrastructure for governance and lifecycle management—making on‑device inference a practical option across edge vision, decentralized AI, and data platform use cases.