Generative AI with local compute describes architectures and products that move model inference, data processing, or agent execution from remote cloud servers onto end‑user machines, private infrastructure, or edge devices. Anthropic’s “Use Your Computer” capability exemplifies this shift by enabling Claude to access or offload work to a user’s local environment so sensitive inputs and context can be processed without cloud data exfiltration. As of 2026‑04‑02, enterprises and developers are prioritizing on‑device and self‑hosted options to reduce latency, meet compliance and residency requirements, and retain tighter control over IP and telemetry. The space spans compact, edge‑ready models (for example Stability’s Stable Code family and open‑source variants like nlpxucan/WizardLM and Meta’s Code Llama) and tools that feed local context securely to models (EchoComet, Tabnine, JetBrains AI Assistant). Platforms such as StackAI and Xilos target enterprise agent orchestration and visibility, while Qodo focuses on code quality, testing and SDLC governance in multi‑repo environments. Together these categories form a stack: lightweight or self‑hosted models; local context ingestion and IDE integration; agent orchestration and observability; and governance controls to enforce policies and audit behavior. Key trends driving adoption include model optimization for small footprints, hybrid cloud/local deployment patterns, growing regulatory scrutiny of data flows, and demand for provenance and audit trails for agentic activity. Implementers must balance performance and UX with security: local compute reduces cloud exposure but introduces patching, endpoint hardening and policy challenges. Evaluating alternatives involves tradeoffs across model capability, deployment complexity, governance tooling and developer ergonomics—making this an active area for product and security teams planning AI beyond the public API model.