Security

TL;DR LM Studio running on your own hardware eliminates per-token billing, data transmission to Google’s infrastructure, and dependency on internet connectivity. For teams processing sensitive customer data, financial records, or proprietary code, keeping inference local satisfies GDPR Article 32 requirements for data minimization without complex data processing agreements. Google’s Vertex AI and Gemini API charge for every API call. LM Studio downloads models once from Hugging Face, then runs them indefinitely on your hardware with zero recurring costs. A mid-range workstation with 32GB RAM and an RTX 4070 handles most 7B-13B parameter models at acceptable speeds for internal tooling, documentation generation, and code review workflows. ...

Running llama.cpp Server for Local AI Inference TL;DR llama.cpp server mode transforms the C/C++ inference engine into a production-ready HTTP API server that handles concurrent requests with OpenAI-compatible endpoints. Instead of running single inference sessions, llama-server lets you deploy local LLMs as persistent services that multiple applications can query simultaneously. ...

Securing Your Local Ollama API TL;DR By default, Ollama exposes its API on localhost:11434 without authentication, making it vulnerable if your network perimeter is breached or if you expose it for remote access. This guide shows you how to lock down your local Ollama deployment using reverse proxies, API keys, and network isolation techniques. ...