Anyone packaged one of these in an iPhone App? I am sure it is doable, but I am curious what tokens/sec is possible these days. I would love to ship "private" AI Apps if we can get reasonable tokens/sec.
Gemma 3 QAT Models: Bringing AI to Consumer GPUs
51–60 of 286 posts
Re: Gemma 3 QAT Models: Bringing AI to Consumer GPUs
#52Re: Gemma 3 QAT Models: Bringing AI to Consumer GPUs
#53Except this being lighter than the other models, is there anything else the Gemma model is specifically good at or better than the other models at doing?
Re: Gemma 3 QAT Models: Bringing AI to Consumer GPUs
#54I 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…
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…
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.
Re: Gemma 3 QAT Models: Bringing AI to Consumer GPUs
#55It seems pretty impressive - I'm running it on my CPU (16 core AMD 3950x) and it's very very impressive at translation, and the image description is very impressive as well. I'm getting about 2.3token/s on it (compared to under 1/s on the Calme-3.2 I was previously using). It does tend to be a bit chatty unless you tell it not to be; pretty much everything it'll give you a 'breakdown' unless you tell it not to - so f…
Re: Gemma 3 QAT Models: Bringing AI to Consumer GPUs
#56Earlier quoted context omitted.
How many times do I have to say this? Ollama, llamacpp, and many other projects are slower than vLLM/sglang. vLLM is a much superior inference engine and is fully supported by the only LLM frontends that matter (sillytavern). The community getting obsessed with Ollama has done huge damage to the field, as it's ineffecient compared to vLLM. Many people can get far more tok/s than they think they could if only they kne…
Why is sillytavern the only LLM frontend which matters?
Re: Gemma 3 QAT Models: Bringing AI to Consumer GPUs
#57It seems pretty impressive - I'm running it on my CPU (16 core AMD 3950x) and it's very very impressive at translation, and the image description is very impressive as well. I'm getting about 2.3token/s on it (compared to under 1/s on the Calme-3.2 I was previously using). It does tend to be a bit chatty unless you tell it not to be; pretty much everything it'll give you a 'breakdown' unless you tell it not to - so f…
What are you using to run it? I haven't got image input working yet myself.
./build/bin/llama-gemma3-cli -m /discs/fast/ai/gemma-3-27b-it-q4_0.gguf --mmproj /discs/fast/ai/mmproj-model-f16-27B.gguf -p "Describe this image." --image ~/Downloads/surprise.png
Note the 2nd gguf in there - I'm not sure, but I think that's for encoding the image.
Re: Gemma 3 QAT Models: Bringing AI to Consumer GPUs
#58Re: Gemma 3 QAT Models: Bringing AI to Consumer GPUs
#59I 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…
Any tips on generating long output? Like multiple pages of a document, a story, a play or even a book?
Re: Gemma 3 QAT Models: Bringing AI to Consumer GPUs
#60I 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…