Local AI Model Gateway
Self-hosted LLM and diffusion workflow experiments around local inference, quantized models, API wrappers, prompt pipelines, queues, and human review boundaries.
Stack
Python, FastAPI, Docker, local LLMs, Qwen-style open-weight models, GGUF/quantized inference, SDXL-style diffusion workflows, API wrappers
Proof angle
Shows practical AI infrastructure work: running models locally, wrapping them into usable APIs, routing jobs through queues, and keeping operator approval around external actions.
Overview
Local AI Model Gateway is a private lab/workflow layer for testing self-hosted LLM and image-generation workflows. The focus is not training foundation models. The focus is practical deployment: model loading, hardware constraints, quantized inference, API surfaces, queue handling, prompt presets, output review, and integration into internal tools.
Where this is relevant
Relevant for AI workflow tools, local/private inference, internal automation, content review systems, model-assisted triage, image-generation workflows, and teams that want AI features without handing every step to a black-box SaaS.
What it shows
- local and open-weight model experimentation
- quantized model deployment constraints
- API wrapper design
- batch queue handling
- prompt-pipeline structure
- diffusion and image-generation workflow handling
- human review boundaries
- practical integration thinking