Engineered for the edge, without the internet.
This project is a local-first AI development environment designed to run large language models and vision models completely offline. It uses tools such as Ollama, Hugging Face, Open WebUI, DeepSeek, LLaMA 3.1, and Gemma to create a private, air-gapped AI stack. The main idea is to demonstrate model orchestration, hardware-accelerated inference, and integrated text-plus-vision workflows in an isolated environment.
Ollama runtime orchestrating multiple models (LLaMA, DeepSeek, Gemma), Hugging Face model repository integration, Open WebUI interface, and GPU-accelerated inference.
GPU memory management
Model quantization trade-offs
Inference performance optimization
Multi-modal workflow integration
Large language model deployment
GPU computing and optimization
Model quantization techniques
Offline AI infrastructure
AI/ML Engineer & Infrastructure Specialist
Designed and implemented enterprise-grade infrastructure that scales reliably, meets production requirements, and demonstrates best practices in DevOps and cloud engineering.
Let's discuss how to apply these DevOps and infrastructure patterns to your needs.