On device I would gladly pay for good hardware - it's my machine and I'm using as I see fit like an IDE.

I'm not convinced that local LLMs use less electricity either. Per token at the same level of intelligence, cloud LLMs should run circles around local LLMs in efficiency. If it doesn't, what are we paying hundreds of billions of dollars for?

I think local LLMs will continue to grow and there will be an "ChatGPT" moment for it when good enough models meet good enough hardware. We're not there yet though.

Note, this is why I'm big on investing in chip manufacture companies. Not only are they completely maxed out due to cloud LLMs, but soon, they will be double maxed out having to replace local computer chips with ones that are suited for inferencing AI. This is a massive transition and will fuel another chip manufacturing boom.

Recently I built a graphRAG app with Qwen 3.5 4b for small tasks like classifying what type of question I am asking or the entity extraction process itself, as graphRAG depends on extracted triplets (entity1, relationship_to, entity2). I used Qwen 3.5 27b for actually answering my questions.

It works pretty well. I have to be a bit patient but that’s it. So in that particular use case, I would agree.

I used MLX and my M1 64GB device. I found that MLX definitely works faster when it comes to extracting entities and triplets in batches.