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

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

#101
post #71

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 a 128gb m4 max macbook pro i've been wanting to tinker with this stuff but genuinely never find the time. any mac users in here running similar to the above that can share their experience? i always see great debates with local stuff but the space is constantly moving goalposts and all the vernacular is pretty unfamiliar to me. i'd love to understand what people with objective experience feel they've traded aw…

If you have a 128GB Mac you really ought to try out: https://github.com/antirez/ds4 by the creator of redis. This is probably as close to it gets to state-of-the-art local LLM + agentic coding.

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

#102

For personal use, yes. I replaced a $100/m subscription to claude in favor of running pi harness pointed at unsloth studio, using both qwen (unsloth/Qwen3.6-35B-A3B-MTP-GGUF) and gemma (unsloth/gemma-4-26B-A4B-it-GGUF) models, depending on my mood. I have a machine I built about 5 years ago with dual RTX3090s in it (I was going to build a new gaming machine anyways, and the llama release had just dropped so I tacked…

2x RTX3090 are around $4400. Without any electricity costs or other parts, that's 3.6 years of $100/m claude.

Yes, today is not a great time to purchase hardware.

When I bought, I paid $850 a piece. And I needed one anyways for the gaming I was going to do.

My guess is the next good time to buy is going to be 24-36 months from now, depending on how the AI bubble goes.

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I'll add to this, I personally don't like Apple hardware (not so much related to the hardware as their company philosophy) but their machines with unified memory (or AMDs latest unified memory offerings) get pretty equivalent speeds to my 3090s, and are probably a much better modern entrypoint to local llms.

There's a reason the joke is that Silicon Valley software devs bought up all the Mac minis for OpenClaw.

You can get a 48gb unified RAM M4 pro mac mini for ~2k. If you're not going to do much else with the machine, it's what I'd pick as my budget inference device right now. Spend a year of claude now, get ~150tok/s for the next decade (plus) for ~free.

If you want more capable and are willing to spend a little more, go with the newer Ryzen AI Max+ 395 machines.

You'll spend less on power too.

My last suggestion would be to go buy an RTX3090 at this point. You can do a lot better for a lot cheaper.

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

#103

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/

"Quality is like running edge models from 8-12 months ago." That sounds great for hobbyists but IMHO it wasn't until Opus 4.6 was released six months go (Dec 25, 2025) that we had a model good enough for professionals to use as a primary driver of their coding agents. That seems to be the threshold worth aiming for.

So thalen it might be 6-8 months to get to useable on a local open model? Of course state of the art will be a year ahead, a generation at the current pace.

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

#104

I don't think you're going to get many "true" answers to this. The opportunity cost of not using the latest and best models is just too much right now. Every month I research this and come to the same conclusion: the time, effort, and cost required to get local models (and the coding tools around them) to perform even close to Claude Code with sonnet/opus just not worth it right now. If it was, it would be distributi…

At some point, there will come a saturation point for that "Opportunity cost FOMO train ride", and I think we are already past that point. Mythos class models are a whole different beasts and cutting edge on reasoning but not much use for the problem domains most developers are trying to solve.

The present Sonnet/Opus versions (~4.8) will likely be what everyone in the enterprise might end up using eventually. And even though local models aren't there yet, there are budget alternatives from the families of DeepSeek, Kimi, GPT, MiniMax, etc. available through APIs of NVidida, OpenRouter, Groq, etc. which are very much Sonnet grade.

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

#105

For personal use, yes. I replaced a $100/m subscription to claude in favor of running pi harness pointed at unsloth studio, using both qwen (unsloth/Qwen3.6-35B-A3B-MTP-GGUF) and gemma (unsloth/gemma-4-26B-A4B-it-GGUF) models, depending on my mood. I have a machine I built about 5 years ago with dual RTX3090s in it (I was going to build a new gaming machine anyways, and the llama release had just dropped so I tacked…

2x RTX3090 are around $4400. Without any electricity costs or other parts, that's 3.6 years of $100/m claude.

You can get 60tps with three 1080tis and the sparse model, and I bet two 16gb 5060tis would do the same for ~1200. One 3090 is enough for a useful system, even on an old am4 host.

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

#106
post #50

Yes, Qwen3.6-35B-A3B on a Strix Halo 128GB (Bosgame M5). I have way too much VRAM forme such a model but Qwen never released the 122B version of Qwen3.6, which is the best class of model for my hardware. But at the same time my electricity bill is negligible, this is originally a laptop chip and it shows, it consumes almost nothing while idle and a little above 120W during prompt processing. And Qwen3.6 has been surp…

Have you tried the 27B dense version? It’s way better for coding.

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

#108

For personal use, yes. I replaced a $100/m subscription to claude in favor of running pi harness pointed at unsloth studio, using both qwen (unsloth/Qwen3.6-35B-A3B-MTP-GGUF) and gemma (unsloth/gemma-4-26B-A4B-it-GGUF) models, depending on my mood. I have a machine I built about 5 years ago with dual RTX3090s in it (I was going to build a new gaming machine anyways, and the llama release had just dropped so I tacked…

Did you double the tokens per second by adding a second GPU or was the increase significantly less?

No real change in inference speed. It basically just allows me to slot in more context or a bigger model.

A single RTX-3090 will do approximately the same tok/s, but it won't fit the entire 300k context in VRAM.

Sometimes that matters, a lot of times it doesn't.

On the speed front - MOE models are great. Biggest perf difference in modern models is the move to MOE architectures.

I get very similar quality from the both the Gemma-4 31B dense model, and the Gemma-4 26B MOE model (both at Q4 quant) but the MOE version runs at ~3 times the speed (150tok/s vs 46tok/s).

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

#109
Models that you can run at home (Like Qwen 35B) aren't remotely close to Opus or GPT 5.5. Not even close. The only open models that are in that neighbor are around 1T params, so forget about running at home.

It's kind of like driving a shitbox. It can often drive you from A to B, and some people will try to convince you it's fine. It's not.

There's no logical reason other than absolutely requiring the privacy, doing it for fun, or niche use cases like airplanes and so on. If you can't spend the insanely subsidized $20 for codex, you can use an API for chinese models which will run circles around these tiny models.

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

#110

I tried gemma-4-26B-A4B just to see if it could help me read/sort my emails on a relatively under-powered setup (16GB VRAM + 32GB RAM) and it's not going well.. the model burns 24K tokens just on searching for the right tool and then dumps the email contents into context - i tried to get it to use code-mode to save context but the code-mode implementation can't save files so it was useless and im going to try to swit…

Perhaps try a different model? Just from anecdotal experience, I find that the Gemma models smaller than 31B do not tool call as often as they should.

Some of the benchmarks appear to back this up [0]

Of course, a lot depends how you are using it (inference parameters, harness, prompting, etc.), but the model is quite important too.

[0]: https://artificialanalysis.ai/models/open-source/small?model...

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