Confidential on‑chain AI execution networks and privacy‑preserving AI platforms bring together decentralized task marketplaces, confidential computing, and on‑device LLM inference to run AI workloads without exposing sensitive data. As of 2026-02-25 this topic is timely because growth in edge AI, stronger privacy regulation, and broader hardware support for TEEs and secure enclaves have increased demand for architectures that keep data local, provide verifiable execution, and enable monetized, auditable AI services. These systems typically combine several patterns: on‑device inference (running models or smaller LM contexts on phones and edge devices), local retrieval‑augmented generation (RAG) and index servers that never upload raw documents, and on‑chain coordination or payment rails for task discovery and settlement. Example tool classes include MCP (Model Context Protocol) servers that enable local or on‑prem RAG and multi‑model orchestration. Local RAG is a privacy‑first, offline semantic search server that indexes PDFs and serves MCP clients; Minima provides a containerized on‑prem RAG stack with multiple deployment modes and optional LLM components; FoundationModels integrates Apple’s FoundationModels into MCP for macOS text generation; and Multi‑Model Advisor queries and synthesizes perspectives from multiple Ollama models. When paired with confidential on‑chain networks (for example smartphone orchestration networks like Acurast), these tools let sensitive retrieval and inference remain on device while using decentralized ledgers for discovery, incentives, and auditability. Practical tradeoffs include model size vs. latency, hardware TEE availability, and the engineering needed to keep indexes and embeddings local. The result is a pragmatic privacy‑centric stack for regulated data, enterprise on‑prem needs, and user‑facing edge AI services.