I use Ollama with qwen-coder-2.5, integrated with Cline in VSCodium. Works great.
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Qwen coder model from Huggingface, following the instructions there to run it in llama.cpp. Once that’s up: OpenCode and use the custom OpenAI API to connect it.
You’ll get far better results than trying to use other local options out of the box.
There may be better models potentially but I’ve found Qwen 2.5 etc to be pretty fantastic overall, and definitely a fine option beside Claude/ChatGPT/Gemini. I’ve tested the lot and it’s usually far more down to instruction and AGENTS.md instructions/layout than it is down to just the model.
Do you mind sharing your agents md?
The main thing that has stopped me from running models like this so far is VRAM. My server has a RTX 4060 with 8GB, and not sure that can reasonably run a model like this.
Edit:
This calculator seems pretty useful: https://apxml.com/tools/vram-calculator
According to this, I can run Qwen3 14B with 4B quant and 15-20% CPU/NVMe offloading and get 41 tokens / s. It seems 4B quant reduces accuracy by 5-15%.
The calculator even says I can run the flagship model with 100% NVMe offloading and get 4 tokens / s.
I didn’t realize NVMe offloading was even a thing and not sure if it actually is supported or works well in practice. If so, it’s a game changer.
Edit:
The llama.cpp docs do mention that models are memory mapped by default and loaded into memory as needed. Not sure if that means that a MoE model like qwen3 235b can run with 8GB of VRAM and 16GB of RAM, albeit at a speed that is an order of magnitude slower like the calculator suggests is possible.
This. Llama.cpp with Vulkan backend running in docker-compose, some Qwen3-Coder quantization from huggingface and pointing Opencode to that local setup with a OpenAI-compatible is working great for me.
I've not found them useful yet for more than basic things. I tried Ollama, it let's you run locally, has simple setup, stays out of the way.
I have heard good things about LM Studio from several professional coders and tinkers alike. Not tried it myself yet though, but I might have to bite the bullet because I can't seem to get ollama to perform how I want.
TabbyML is another thing to try.
Thanks for the reply!
I had noticed TabbyML but something about their wording made me rethink and then the next day I saw a post on here regarding the same phrasing, I decided to leave it alone after that
Yeah I tried tabby too and they had like a mandatory "we share your code " line and I hoped out. Like if you're going to do that I might as well just use claude
LM Studio in combination with Kilo Code for IDE integration works pretty nicely locally. Here is a good video covering the basics to get you going: https://www.youtube.com/watch?v=rp5EwOogWEw
I get good mileage out of the Jan client and Void editor, various models will work but Jan-4B tends to do OK, maybe a Meta-Llama model could do alright too. The Jan client has settings where you can start up a local OpenAI-compatible server, and Void can be configured to point to that localhost URL+port and specific models. If you want to go the extra mile for privacy and you're on a Linux distro, install firejail from your package manager and run both Void and Jan inside the same namespace with outside networking disabled so it only can talk on localhost. E.g.: firejail --noprofile --net=none --name=nameGoesHere Jan and firejail --noprofile --net=none --join=nameGoesHere void, where one of them sets up the namespace (--name=) and the other one joins the namespace (--join=)
I recommend llama.cpp instead of LM Studio.