But regardless, the main point of the gap is resources

What makes you think we won't have the resources in the future?

Any model that can run on 16GB or less, is not going to be any close in real world tasks, to any other cloud based model. It just cannot be.

Well you can compare Gemma 4 running in LM Studio on an average gaming PC to ChatGPT3.5 and you tell me? Or is your benchmark purely based on right at this very moment between open source models today vs cloud today?

For reference Gemma 4 is 26 billion parameters, gp3 thought to be over 175 billion and of course had no optimisations like MoE, it was searching its entire library every single question so was rather slow as well

We know as well that there is no slow down in pushing for optimisations, Deepseeks initial release was the initial driver for you don't have to just scale up using hardware alone

https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/

They're also pushing with Chinese native chips from Huawei trying to diversify away from nvidia holding the crown

The problem I've got is that you all have a god of the gaps, the conversation I was having 3 years ago was different to 2 years ago was different to 1 year ago, I was told AI could never do songs good enough then suddenly people were worried they couldn't tell the difference, then they said they could never do movies, now apparently not only is it good enough it's hilarious

https://www.youtube.com/watch?v=fgHn7PI55J4

The open source LLM's we have today are incredible and in the last few months we've had Qwen, GLM, Nemotron/Nvidia, Mistral, Google and heeaaps of others released, it feels like you're just looking for a reason to be dour and pessimistic but that's just me

Any way I'm off to sleep, have a good one :)