Yeah, I didn't write this as a proper developer guide. My screen recording started getting loads of favourites and I started getting messages asking about how I set it up, so just through up a quick rundown of how I setup this test.

I little just saw the Unclothe announcement about "Double the speed" and thought "Ha. I wonder if that will get it fast enough I'd actually be prepared to use it" and had a go at setting it up.

I'd done tests before last year with things like Devstral, but they were always both so slow and dumb, I didn't want to bother.

This finally hit the "wow, this is useable" level of both speed and intelligence.

llama.cpp includes tools for that, what you are looking at is to have a prefill before token generation to measure it properly. Increasingly also, measuring token generation speed at longer context (32k or 64k) is important too.

As for oprncode, doesn't the system prompt eat too much of the context? Local models are really constraint in regards contex, and opencode AFAIR uses a 10k of it or some thing close.

-hfd for the draft model.

The Gemma 4 MLX builds I have found so far have been slower at the same quantisation and much slower with MTP.

The built-in web UI for llama.cpp is really quite good once you have chosen your model. Otherwise I quite like LM Studio for tinkering.

One thing I would say is that both Gemma-4 and Qwen 3.6 simply do not need a large chunk of the typical opencode system prompt. Better off without it.

I'm not Googling much of anything anymore. 9/10 times the information is awful, it's hard to parse out of whatever other spam it's surrounded by. Meanwhile, Claude will just do the thing one-shot or with a tiny bit of refinement.

The gateway to knowledge and getting stuff done is the LLM.

Google Search is a dinosaur.

It feels like we're living a century into the future. Not even smartphones were this cool.

But if you just want to play around rather than code, you really might find the Gemma 4 12B model worth mucking about with just so you've gone through the steps. Especially if you want to muck about with image analysis or audio transcription.

If you're writing PHP I think you could even find it good enough. I've been modestly surprised. You can do that basic fiddling with the Edge AI Gallery app, which can enable thinking and has a customisable system prompt and some agent support.

You could also try the 14B Deepseek R1.

Honestly even if it is not good enough, if you are anything like me, I think you'll find that going through this process is really quite educational — it has made a lot of things more concrete for me in a way that I have found reassuring and valuable.

omlx + gemma 12b 6 bit + pi

it’s feasible for sure

MoEs for speed (qwen 35b, cohere 30b, gemma 26b)

Dense for more methodical work (qwen 27b [reigning champ], gemma 31b, gemma 12b)

MoE i recommend 5bit+

Dense i think 4 bit is okay

Play with your context size, you don’t really need that much, have lazy loading for tools and mcps

my pi extensions for anyone looking for a skinny quick setup, i have use `--no-skills` right now too:

    "npm:pi-codex-goal",
    "npm:pi-simplify",
    "npm:pi-mcp-adapter",
    "git:github.com/elpapi42/pi-minimal-subagent",
    "npm:@wierdbytes/pi-statusline",
    "npm:@aliou/pi-guardrails",
    "npm:pi-lens",
    "npm:@juicesharp/rpiv-todo",
    "npm:pi-hashline-readmap",
    "npm:@mrclrchtr/supi-review",
    "npm:pi-cmux",
    "npm:@mrclrchtr/supi-context",
    "npm:pi-tool-search"

Also there are smaller, still usefull models that can run on 8GB or less.

It solve the "I'm coding on the plane and need to look up this thing I've forgotten" problem, for me at least

So there is no value in testing quality of answers, but there is value in testing token speed.

You just have to have correct expectations.

But there is an incentive not to use it if you want to write an article that uses only open-source tools, because it isn't.

Basically one has two real choices for local LLMs: llama.cpp (if single user) or vLLM (if multi-user/enterprise).

oMLX does the caching I need to fit models that are near gross memory, and it handles most of the work in finding usable models. After cobbling together various solutions over months, I now just use oMLX, often from Xcode. I can tell the difference between Gemma-4 (local/free) and Claude (paid) only on the largest tasks.

Also Ollama has other issues (like forgetting what it really is - a wrapper).