Ask HN: Has anyone replaced Claude/GPT with a local model for daily coding?

The problem with this question is that it encompasses a huge spectrum of capabilities and expectations. If you can only run an 8B model and expect it to be good at vibe coding / one shotting things you're going to have a bad time.

If you're able to run a model on the scale of ~30B, you can find that with a reasonably scoped and well defined task they do very well. I've found both Gemma4-31B and Qwen3.6-27B to be the best in this range at the moment. You can swap in the MoE models for faster inference, but they are noticeably worse at most tasks. They can one-shot / vibe code tasks with small scope, but still do much better with guidance.

If you really want frontier-like capabilities, you'll probably need at least 128GB of memory and either huge compute or a lot of patience. Most people just don't have either the money or the patience to make these local models work.

The patience required for local model usage goes far beyond just waiting for tokens though. It takes a lot of effort to get things configured and working properly for your workflow and hardware.

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Yes. Llama.cpp + Qwen3.6-35b (MTP) + OpenCode is quite capable and runs on a single RTX 3090 and is faster than most cloud models. Quality is like running edge models from 8-12 months ago. Setup details at https://github.com/pierotofy/LocalCodingLLM/

I have been running this stack since well before Claude Code became popular. It works OK but I've found it to be very slow; and despite having a big context window, it seems to lose track of what it's working on and goes down a rabbit hole (or just wastes tokens trying to use the web browser) for hours and is hard to get back on track. I even tried spinning up two sub-agents but even after years of trying to prompt them, they are almost useless in terms of coding ability, so that is looking to be a waste of spending at least so far but maybe the model will improve as time goes on.

My strix halo board is feeling more useful and less toylike with the recent performance gains combined from MTP, better quantization, and generalized performance improvements across the stack. For example, I can run Unsloth's Gemma4-31B 4-bit QAT model with around 30tg and 200pp. I don't find that to be too slow at all. Particularly because it's nearly full accuracy and good enough for a lot of different stuff I throw at it.

I think it also helps that I'm using my machine to do home server stuff. It excels at all of the traditional workloads. Then I can lean on the AI to help with automation here and there. I find it deeply satisfying.