Gemma 3 QAT Models: Bringing AI to Consumer GPUs

I think gemma-3-27b-it-qat-4bit is my new favorite local model - or at least it's right up there with Mistral Small 3.1 24B.

I've been trying it on an M2 64GB via both Ollama and MLX. It's very, very good, and it only uses ~22Gb (via Ollama) or ~15GB (MLX) leaving plenty of memory for running other apps.

Some notes here: https://simonwillison.net/2025/Apr/19/gemma-3-qat-models/

Last night I had it write me a complete plugin for my LLM tool like this:

  llm install llm-mlx
  llm mlx download-model mlx-community/gemma-3-27b-it-qat-4bit

  llm -m mlx-community/gemma-3-27b-it-qat-4bit \
    -f https://raw.githubusercontent.com/simonw/llm-hacker-news/refs/heads/main/llm_hacker_news.py \
    -f https://raw.githubusercontent.com/simonw/tools/refs/heads/main/github-issue-to-markdown.html \
    -s 'Write a new fragments plugin in Python that registers
    issue:org/repo/123 which fetches that issue
        number from the specified github repo and uses the same
        markdown logic as the HTML page to turn that into a
        fragment'

It gave a solid response! https://gist.github.com/simonw/feccff6ce3254556b848c27333f52... - more notes here: https://simonwillison.net/2025/Apr/20/llm-fragments-github/

Can you quote tps?

More and more I start to realize that cost saving is a small problem for local LLMs. If it is too slow, it becomes unusable, so much that you might as well use public LLM endpoints. Unless you really care about getting things done locally without sending information to another server.

With OpenAI API/ChatGPT, I get response much faster than I can read, and for simple question, it means I just need a glimpse of the response, copy & paste and get things done. Whereas on local LLM, I watch it painstakingly prints preambles that I don't care about, and get what I actually need after 20 seconds (on a fast GPU).

And I am not yet talking about context window etc.

I have been researching about how people integrate local LLMs in their workflows. My finding is that most people play with it for a short time and that's about it, and most people are much better off spending money on OpenAI credits (which can last a very long time with typical usage) than getting a beefed up Mac Studio or building a machine with 4090.

My tooling doesn't measure TPS yet. It feels snappy to me on MLX.

I agree that hosted models are usually a better option for most people - much faster, higher quality, handle longer inputs, really cheap.

I enjoy local models for research and for the occasional offline scenario.

I'm also interested in their applications for journalism, specifically for dealing with extremely sensitive data like leaked information from confidential sources.