Re:Gemma 4 QAT models: Optimizing compression for mobile and...
by dofm on Friday June 05, 2026 @05:10PM
(#48418344)
The 26B model is really surprising, and it is impressively concise — it spends a lot less time dithering than Qwen3.6.
Re:Gemma 4 QAT models: Optimizing compression for mobile and...
by steno132 on Friday June 05, 2026 @04:45PM
(#48418003)
Practically if you're running a small personal automation project you're not going to want to waste a lot of time configuring and tuning a local model. You want to build the automation and move on.
If you're building a automation as a company you definitely won't want to take on the long term maintenance overhead of running your own models for some automation project.
Re:Gemma 4 QAT models: Optimizing compression for mobile and...
by adam_arthur on Friday June 05, 2026 @04:45PM
(#48418015)
These small models exist in the cloud and are/will be priced commensurately to their size.
Your claim is effectively that companies don't care about operational/cloud costs. Even pre-LLM, companies regularly assessed and tried to pare down cloud spend.
I don't think many realize that most LLM embedded automation, pipelines, products will soon be able to run extremely cheaply on models < 100B parameters.
Frontier models will be used for coding/creation use cases, yes. But for all the pseudo-deterministic, pipeline, analysis style things there will be no practical benefit to running frontier models, only additional cost.
Gemma 4 26B outperforms most 100-200B models that I've tested for reasoning and structured output.
Gemma 4 12B can consistently select where to click on browser images given a minimal prompt, and do so very quickly.