u/Adorable_Weakness_39

There's been huge interest in local LLMs recently with the leap in their capabilities and intelligence with Qwen 27B being not far behind the best models from last year (see the image) whilst able to run on consumer hardware.

https://preview.redd.it/11xhf30sjb0h1.png?width=1112&format=png&auto=webp&s=2375f308299ec9dfaf1dd16830af971a6d10b413

That led me to find that there's a real problem with people setting up their local LLMs and performance is being left on the table by bad default settings. The default Ollama config gave my 18 tok/s on the same hardware I got 70 tokens/s. Also, models change every month, and unless you're keeping track of every new model and inference optimisation, you get left behind.

So I built OpenJet to combine the inference backend with the frontend coding agent harness like Claude Code to build a local-first coding harness. This means the backend config is managed automatically according to your hardware, and the agent harness is designed specifically for being on your machine - no Cloud API calls or expensive plans to manage.

https://preview.redd.it/wr54dlgtkb0h1.png?width=961&format=png&auto=webp&s=bc904c4ddbebe01546b236ceeededb14e6f67c63

I've tested it on my RTX 3090 and got 70 tok/s for Qwen3.6-27B.

If you want to give it a go or join the Discord community, or just have a look, here's the link:

https://openjet.dev/

I hope to see what you build.