1
Parallel coding agents in VS Code
You run three agents at once: one refactors, one writes tests, one reviews. On a single-slot server they take turns, and each one sits idle while the others think.
What you set up- A coding model on the machine with the most GPU memory, on vLLM where you can
- A smaller, faster model on a second machine for quick edits and completions
- The Connect page settings pasted into Cline, Roo Code or Continue
What you get- Requests from all three agents served at the same time
- Each agent picks a model by name and never needs to know which machine answered
- The benchmark shows how the coding model holds up at 1, 16 and its maximum number of parallel requests
2
One endpoint for a small team
Four developers share a few machines. Each wants their own key, nobody wants to manage servers, and somebody has to know what is running.
What you set up- One API key per person and per tool
- Models placed on the machines that fit them, checked before every start
- A scheduled backup of models, settings and keys to a network share
What you get- One base URL for the whole team
- A live Overview of what runs where and how much memory each machine has left
- Keys you can hand out and take back without touching anyone's setup
3
The whole family, at the same time
Homework help in a chat app, a parent drafting an email, a teenager experimenting with a script. Everyone asks at once, on hardware that is already in the house.
What you set up- A friendly chat model on the Mac that sleeps every evening
- A larger model on the old gaming PC for harder questions
- Any chat app that speaks the OpenAI API, pointed at one address
What you get- Answers that do not queue behind each other
- Nothing leaves the house: requests go to your own machines
- Models that come back on their own after a restart, and stay stopped when you stop them
4
Comparing models on your own hardware
A new model is out and everyone says it is faster. Is it, on your card, with your context length, with your workload?
What you set up- Both models side by side, on the same machine or on two
- The workload preset that matches how you use them: chat, agents and tools, long documents or embeddings
- The tuner, to search vLLM settings for the one you keep
What you get- Measured numbers from your own machines, kept per model
- A winner the tuner re-measures before naming it
- No change until you press Apply