I opted to buy a normal 32GB laptop for this very reason. I know how loud and hot the GPUs in my desktop run when running even smallish models like Qwen 27B or Gemma 4 31B (which is a better model for most than Qwen 3.6, despite the benchmarks). I also have a Strix Halo which doesn't get loud, because it has a single huge fan, but it does get hot. So, there's no way a laptop could work as hard as models make them work, and not be unbearable. Tiny fans trying to remove all that heat? They gotta be screaming. No reason to spend all that money on a laptop that I couldn't realistically make use of. I do run a lot of VMs on my desktop, but I can get to those on a VPN.
It's a nice idea to run a model on a laptop so you can work anywhere...but, that's a job for models in the cloud. Not much data has to traverse the network, so it's not a big deal. Or one could also setup a VPN so you can reach a self-hosted model on a big box at home for things that require data privacy.
All that said, there are models that work great on very small devices for some tasks and won't work it to death. Gemma 4 12B QAT 4-bit runs on a 16GB device, maybe even smaller, including a tablet. It's the best self-hostable vision model I've tested for my purposes (categorization, identification, labeling, type stuff), beating much larger models. It's also a decent conversationalist with good prose but it doesn't know much of anything (not a lot of the world fits in 7GB), so it needs search if you want to use it for research. It's a pretty good tool user. I definitely wouldn't want to use it for code, though, beyond very simple stuff.
Gemma is better than Qwen at everything except coding, in all my evaluations. Which is a shame because that is what I use them for!
I have a M1 Macbook Pro...with only 16gb and I struggled with Qwens2.5-14b trying to do large projects. I loved Qwen but I had to try and do something different. So I switched to Gemma4-12b which looking at it now, seems more like a downgrade than an upgrade.Can you refer me to any Qwen coding models that wont choke my poor 16gb and also connect contextually? I need that context. I love the laser point focus, but I need context and basic understanding of that context.
I haven't run a proper eval, but I've been getting better luck with Qwen models than Gemma on plant and animal identification using vision.
I do like Gemma for translation, however.
It would be great if the Gemma folks would release a code-focused model. Probably won't happen, but it's fun to dream.
The Ornith folks say they're doing that, but haven't released the Gemma-based 31b yet (https://github.com/deepreinforce-ai/Ornith-1). But, also, the Qwen-based 35b MoE Ornith version performs worse than Qwen 3.6 and Qwen AgentWorld on my benchmarks (which are focused on finding security bugs, so not exactly the same as agentic coding, but closely related skills).
That said, the reason they're able to release Ornith branded post-trains of both Gemma and Qwen is because they're open weights under a friendly license. Someone, not just Google, could make a coding focused Gemma post-train. I don't think it's actually much weaker than Qwen 3.6 for coding; Gemma 4 31b outperforms Qwen 3.6 27b by a wide margin on security bug hunting (at least for the specific bugs in my benchmarks, which are mostly relatively difficult bugs from the Mythos-reported bugs).
I'd really love to see a bigger MoE from Google, though. A 70b or 120b MoE would likely be super fun.
Ya, doesn't seem to be google's focus at all, right?
gemma is also worse for tool calling. not just coding
That is because they use a different tool calling format than most other models. Unsloth quants fix this in their Gemma releases.
I've never been able to fix the tool calling issues. Running unsloth versions with llama.cpp, constant issues. Have tried many forum fixes, including lots of fixed chat templates, to no avail. It's mostly the edit call that breaks, which often results in "let me just rewrite the whole file from context".
Can you say a bit more about this? The bad tool calling has made me give up on using Gemma for my Hermes and a personal recipe site. I have only downloaded from Ollama.
Ollama is not recommended [0], use llama.cpp or more specifically Unsloth Studio which wraps llama.cpp and which has an API mode you can use to hook into Hermes or another agent. Unsloth make both the Studio and the quants which fix various issues with many models [1] as well as implementing new features like MTP and QAT support much sooner than other teams. In general you should read r/LocalLLaMa as it has a lot of updates regarding local models as the field moves fast.
[0] https://sleepingrobots.com/dreams/stop-using-ollama/
[1] https://github.com/unslothai/unsloth/discussions/4921
You can limit TDP on Strix Halo so it runs between 32 and 45W which seems to be the sweet spot for heat vs speed.