LOCAL BUNKER

A Local Bunker is a self-contained AI infrastructure stack deployed entirely on hardware you own. It runs large language models, embedding models, and inference APIs on-premises — meaning no API calls leave your network, no usage data is collected by third parties, and no provider can revoke your access.

The term "bunker" is intentional. This isn't a convenience setup — it's a hardened, isolated deployment designed for organizations that treat their AI infrastructure as critical and confidential. Think of it as the difference between renting office space and owning the building: when you own the building, nobody can raise your rent, inspect your files, or turn off the lights.

A typical BOTY Local Bunker deployment runs on a dedicated workstation or small server with a modern NVIDIA GPU (RTX 3090, 4090, or A-series) or an AMD equivalent. For CPU-only inference, we use quantized models via llama.cpp. For production loads, we deploy vLLM on multi-GPU setups.

The stack includes: Ollama or vLLM as the inference runtime, Open WebUI or a custom Angular frontend as the chat interface, n8n or LangGraph for workflow automation, WireGuard for encrypted network access from remote locations, and a reverse proxy (Caddy or nginx) for internal routing. Everything is containerized via Docker Compose for reproducibility and easy updates.

We deploy open-weight models selected for your specific use cases. For general reasoning and code, Qwen2.5 72B and Llama 3.1 70B run well on dual-GPU setups. For lightweight tasks, 7B models handle document summarization and classification with sub-second latency. For vision, LLaVA and Qwen-VL provide image understanding without any cloud routing.

Embedding models (nomic-embed-text, mxbai-embed-large) power local RAG pipelines over your internal documents. Speech-to-text uses faster-whisper on the same GPU. The entire stack — from voice input to structured output — runs locally, with latency measured in milliseconds, not seconds.