Local Agentic Coding Stack (Dedicated Mac Mini M4)
To eliminate cloud API latencies, remove per-token subscription costs, and maintain complete privacy for proprietary enterprise codebases, I built and deployed a production-grade local AI infrastructure on a dedicated Mac Mini M4 server.
Hardware & Architecture
- Host System: Apple Silicon Mac Mini M4 with unified memory architecture, delivering exceptional memory bandwidth for quantised LLM inference.
- Model Engine: Ollama running locally, serving quantized weights of Qwen2.5-Coder-32B and LLaMA 3.1.
- IDE Pair-Programming: Continue.dev integrated directly into VS Code, connected to local Ollama endpoints for instant code completion, inline refactoring, and multi-file context debugging.
- Team Interface: Self-hosted Open WebUI providing a ChatGPT-grade chat interface accessible to the entire internal engineering team over a secure Tailscale private mesh network.
Key Outcomes
- Zero Data Leakage: Enterprise proprietary code, database schemas, and business logic never leave the local hardware perimeter.
- Sub-100ms Inference: Ultra-low latency code completions and interactive chat feedback.
- Daily Production Driver: Actively leveraged daily for feature development, refactoring, and documentation on the iSDAT MTS ERP system.
- Cost Savings: Eliminated thousands of dollars in monthly cloud LLM API costs for internal development.