Local LLM Studio: chat and a speed check that stay on the laptop

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

I wanted a local model for the boring stuff: drafting a reply I do not want in someone else's log, checking whether a screenshot is readable, and finding out if a 3B model is actually usable on this laptop. What I had instead was three apps, three ports, and a terminal I had to babysit.

Local LLM Studio is the slow version of that workflow. It sits on 127.0.0.1 and talks to whatever open model server is already running.

Local LLM Studio is a small private front end for a model server on your own machine. It speaks the OpenAI-compatible /v1 API, so the same install works with Ollama, LM Studio, and llama.cpp 's llama-server.

Four jobs, and then it gets out of the way:

The person who gets to be lazier is anyone who keeps a local model around for private drafts and then loses twenty minutes remembering which port is which. Studio binds to 127.0.0.1:8000. Chats, uploaded images, and benchmark rows stay in a./data folder next to the app. There is no account and no telemetry.

A loopback server URL is saved immediately. A private-network URL is saved only after you confirm it, because the prompt then leaves the machine. Public cloud hosts, including https://api.openai.com/v1, are rejected.

There is no deployed link, and that is the product. Studio listens only on loopback, and the URL check turns a public host away.

python -m pip install -e ".[dev]" cd web && npm install && npm run build && cd.. python -m studio Enter fullscreen mode Exit fullscreen mode Open http://127.0.0.1:8000. On Windows, localhost often resolves to IPv6 while Studio listens on IPv4, so use the numeric address.

MIT licensed. The repo is tusharv/local-llm.

Local LLM Studio is a private chat, model list, vision check, and speed benchmark for a model server running on your own machine. It speaks the OpenAI-compatible /v1 API, so it works with Ollama, LM Studio, llama.cpp, and any other local server that implements those routes. Ollama is a preset that can also pull a model, delete a model, and read native vision flags.

Studio binds to 127.0.0.1:8000. Chats, images, and benchmark rows stay in./data. It does not install or start Ollama, LM Studio, or llama.cpp. The first run prints the commands for your operating system. You copy them into a terminal yourself.

The same Studio install is used on macOS, Windows, and Linux.

You need Python 3.11 or newer and Node.js 20 or newer.