I have! I care about data privacy and LLMs being free. I'm using the Pi coding harness but containerized and sandboxed, to make sure it's running completely offline. On my Mac Studio with 128GB RAM (or MacBook with 36GB RAM) I'm using Qwen3.6 35b, with only 3b active parameters so that it runs really fast. I've done a complete redesign for my website's homepage and blog with Django + Wagtail. The latter is interesting, because Wagtail is a bit less well-known, so the agent, without giving it internet access, doesn't always know how to develop for Wagtail. I've used Qwen3.5 122b for when things get more complex. At 10b active parameters, it's significantly slower though.

I've noticed a few things compared to large models like Claude. For starters, you really need to know what you're asking, and be precise; it doesn't do much thinking for you. Any assumptions left open, and it'll take the easiest route to reach the goal (e.g. CSS in HTML), often not the best in terms of architecture.

It gets into loops quite often, and surprisingly often gets the edit tool call wrong, after which it will spend lots of thinking tokens and re-read files instead of retrying (despite the system prompt suggesting so).

Comparing agentic Qwen3.6 35b to Claude Opus is like a junior with knowledge across the board, that you really need to guide, versus a senior that thinks with you on architecture. If Opus gives a 15x speedup, local and fully offline Qwen gives a 5x speedup. Which, given that it's completely free, is still mind-boggling to me :)

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 another used 3090 onto the build), and I get ~150tok/s on either of those models (at UD-Q4_K_XL quant) and can use the entire 300k context length without having to exit VRAM.

To be very clear - it's not as good as claude. But it's free and not so much worse that it matters significantly.

For my personal needs, free beats $100/m.

I also have an openclaw instance pointed at the same inference server, and it's great for that (genuinely solid use-case for local models).

Some example projects

- Replacement launcher for android tvs (with usage monitoring and tracking for kids)

- Custom admin portals for my k8s cluster services

- Custom home assistant integrations/automations (recently some shelly devices for power monitoring and switching)

- Grocery list management and meal planning (mostly via openclaw)

- some custom workflows for 3d asset generation in comfyui.

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Long story short, if you're trying to make money via software... I'd probably still recommend using a paid provider. But the local models are very capable of cool stuff.

About 90% of my coding is on Qwen 3.6 27b and Open Code with some custom skills and Semble. It is NOT as smart as CC or Codex but its enough to get most of my work done. I didn't set out to replace CC and Codex (I have an RTX 6000 so the TPS is faster than I care about, but the RTX 6000 was originally for other work). I only tried this just to see how close you could get to a frontier model for coding as an experiment, but it was good enough that I stuck with it. I still fall back to Codex for really complicated stuff and to polish UI's as that seems to be the weakest element to working in Qwen.This isn't a recommendation because I don't think most people have an RTX 6000 laying around and the cost would be many years of MAX CC or Codex subscriptions, but at least this seems possible. Maybe in a few more years it will even be practical.

Other Notes: I have had to set the compact target to 75% on a 256k context window as once the conversation length goes about 100k I start seeing a drop in the quality and speed. This becomes very problematic after about 150k. I tried Qwen 3.5 122b too but it actually seems much worse at coding than 3.6 27b even though its much larger. Maybe because I am using a 4bit quant or maybe I just don't have it configured correctly? I know 3.6 is newer but I didn't expect it to out perform a model that is much larger from the prior generation. Gemma 4 31b is a good model for other tasks but at least my personal experience is that Qwen outperforms in coding. Nemotron Super 120b is great at a lot of stuff but it also seems to be not as good at coding as Qwen. This was very surprising to me.

  * "commit this on a branch, push, create a PR and assign $nickname for review"
  * "Use the Stripe CLI to download all open and overdue invoices and reconcile them with this CSV export from our bank account."
  * "Use these Elasticsearch credentials to summarise what kind of operations are causing load at the moment."
  * "Tell me if our codebase already supports X and where it's  implemented."

I wish someone would do a benchmark and competition for this kind of work flow so we could figure out what works well.

Like "Here's this consumer grade GPU. Using only this GPU but with whatever models and workflow you want, see how well you can do on xyz benchmark."

Contestants would be given like 1 hour max and scored based on % of questions answered, % of questions correct and total time to finish.

Like "The Local AI challenge"

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/

Not yet. Without pure Apple game or decent GPUs, even with a lot of RAM and threads, all you get is about 30-50 tokens/second, and that's thinking turned off. Without these optimizations your model will have a field day with your MCPs, skills and agent descriptions and you will watch the paint dry before seeing the first output token. Local model serving means you have to fight for every token in your context window, which is quite opposite of what Claude/GPT/Copilot are pushing the industry towards.

I use pi with an RTX Pro 6000 Blackwell to run Gemma 4 31b to do all my agentic coding.

I find it useful.

This side project highlights a similar approach to how I scope and tackle projects at work now:

https://git.theodohertyfamily.com/wg-wrap.git/tree/README.md

https://git.theodohertyfamily.com/wg-wrap.git/tree/CASE_STUD...

You have to apply a lot of careful architecture and TDD to your approach. Eliminate technical risk by tackling hard things early and wrapping them up in a simple, easy to use interface.

I find I can get some projects done 2-3 times faster than if I wrote them by hand. It can also save about 5-10x time on mundane or broadly scoped projects by helping me consolidate and try out ideas very quickly.

Setup-wise, I switch between vLLM using nvidia/Gemma-4-31B-IT-NVFP4 and llama.cpp using unsloth/gemma-4-31B-it-qat-GGUF with MTP. I throttle the GPU power usage to 400W.

My current llama.cpp setup gets token generation rates between 60-150 t/s depending on MTP draft acceptance rates. Prefill is between 1500-4000 t/s depending on context length/depth.

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.

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 distributive enough to be in the news.

Not that I'm discounting someone hasn't already solved this, just trying to Occam razor my way out of diving too deep down rabbit holes.

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.