6 ms·
I haven't seen anybody else post it in this thread, but this is running on 8GB of RAM. It's not the full Gemma 4 32B model. It's a completely different thing fr
by dimmke 6mo ago
I haven't seen anybody else post it in this thread, but this is running on 8GB of RAM. It's not the full Gemma 4 32B model. It's a completely different thing from the full Gemma 4 experience if you were running the flagship model, almost to the point of being misleading.
It's their E2B and E4B variants (so 2B and 4B but also quantized)
https://ai.google.dev/gemma/docs/core/model_card_4#dense_models https://ai.google.dev/gemma/docs/core/model_card_4#dense_mod...
- zozbot234 6mo agoThe relevant constraint when running on a phone is power, not really RAM footprint. Running the tiny E2B/E4B models makes sense, this is essentially what they're designed for.
- trvz 6mo agoIt absolutely is RAM… So much so that this was what made Apple increase their base sizes.
- bigyabai 6mo agoBetween the GPU, NPU and big.LITTLE cores, many phones have no fewer than 4 different power profiles they can run inference at. It's about as solved as it will get without an architectural overhaul.
- Shawnj2 6mo agoDepends on the phone, I have trouble fitting models into memory on my iPhone 13 before iOS kills the app. I imagine newer phones with more RAM don’t have this issue especially with some new flagship phones having 16+ GB of memory