I ran Gemma 4 as a local model in Codex CLI

I spun up a GPU on Runpod and tried the 31b full res and it was really impressive. I'm now using it via the Google API which gives you 1500 requests a day for free, IIRC.

I think it might be a good idea to make some kind of local-first harness that is designed to fully saturate some local hardware churning experiments on Gemma 4 (or another local model) 24/7 and only occasionally calls Claude Opus for big architectural decisions and hard-to-fix bugs.

Something like:

* Human + Claude Opus sets up project direction and identifies research experiments that can be performed by a local model

* Gemma 4 on local hardware autonomously performs smaller research experiments / POCs, including autonomous testing and validation steps that burn a lot of tokens but can convincingly prove that the POC works. This is automatically scheduled to fully utilize the local hardware. There might even be a prioritization system to make these POC experiments only run when there's no more urgent request on the local hardware. The local model has an option to call Opus if it's truly stuck on a task.

* Once an approach is proven through the experimentation, human works with Opus to implement into main project from scratch

If you can get a complex harness to work on models of this weight-class paired with the right local hardware (maybe your old gaming GPU plus 32gb of RAM), you can churn through millions of output tokens a day (and probably like ~100 million input tokens though the vast majority are cached). The main cost advantage compared to cloud models is actually that you have total control over prompt caching locally which makes it basically free, whereas most API providers for small LLM models ask for full price for input tokens even if the prompt is exactly repeated across every request.

In my experience if you're coding or doing something that requires precision, quantizing the kv cache is definitely not worth it.

If you're just chatting or doing less precise things it's 1000% worth it going down to Q8 or sometimes even Q4

Be very careful about using googles apis as a consumer, they have poor rate limiting and ineffective anomoly protection.

I (a hobbyist running a small side project for a dollar or two a month in normal usage, so my account is marked as "individual") got hit with a ~$17,000 bill from Google cloud because some combination of key got leaked or my homelab got compromised, and the attacker consumed tens of thousands in gemini usage in only a few hours. It wasn't even the same Google project as for my project, it was another that hasn't seen activity in a year+.

Google refuses to apply any adjustments, their billing specialist even mixed up my account with someone else, refuses to provide further information for why adjustments are being rejected, refuses any escalation, etc. I already filed a complaint with the FTC and NYS attorney General but the rep couldn't care any less.

My gripe is not that the key was potentially leaked or compromised or similar and then I have to pay as a very expensive "you messed up" mistake, it's that they let an api key rack up tens of thousands in maybe 4 hours or so with usage patterns (model selection, generating text vs image, volume of calls, likely different IP and user agent and whatnot). That's just predatory behavior on an account marked as individual/consumer (not a business).

Get into a venv, and run:

> pip3 install git+https://github.com/ml-explore/mlx-lm.git

> ./venv/bin/mlx_lm.generate --model "$MODEL" --temp 1.0 --top-p 0.95 --top-k 64 --max-tokens 128000 --prompt "Hello world"

Where $MODEL is an unsloth model like:

- unsloth/gemma-4-E4B-it-UD-MLX-4bit

- unsloth/gemma-4-26b-a4b-it-UD-MLX-4bit