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“They will always be behind” Car manufacturers said the same.
It did take decades to catch and surpass US car makers right?
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What is CC and TC? I have not heard these abbreviations (except for CC to mean credit card or carbon copy, neither of which is what I think you mean here).
I figured it out from context clues CC: Claude Code TC: total comp(ensation)
LLMs on device is the future. It's more secure and solves the problem of too much demand for inference compared to data center supply, it also would use less electricity. It's just a matter of getting the performance good enough. Most users don't need frontier model performance.
It isn't going to replace cloud LLMs since cloud LLMs will always be faster in throughput and smarter. Cloud and local LLMs will grow together, not replace each other. 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 lo…
LLMs on device is the future. It's more secure and solves the problem of too much demand for inference compared to data center supply, it also would use less electricity. It's just a matter of getting the performance good enough. Most users don't need frontier model performance.
You could argue that the only reason we have good open-weight models is because companies are trying to undermine the big dogs, and they are spending millions to make sure they dont get too far ahead. If the bubble pops then there wont be incentive to keep doing it.
I could see the model becoming part of the OS.
Of course Google and Microsoft will still want you to use their models so that they can continue to spy on you.
Apple, AMD and Nvidia would sell hardware to run their own largest models.
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It isn't going to replace cloud LLMs since cloud LLMs will always be faster in throughput and smarter. Cloud and local LLMs will grow together, not replace each other. 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 lo…
We are 100% there already. In browser. the webgpu model in my browser on my m4 pro macbook was as good as chatgpt 3.5 and doing 80+ tokens/s Local is here.
LLMs on device is the future. It's more secure and solves the problem of too much demand for inference compared to data center supply, it also would use less electricity. It's just a matter of getting the performance good enough. Most users don't need frontier model performance.
I have journaled digitally for the last 5 years with this expectation. 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 tha…
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"Most users don't need frontier model performance" unfortunately, this is not the case.
Any citations? Because that was my impression, too. I want frontier model performance for my coding assistant, but "most users" could do with smaller/faster models. ChatGPT free falls back to GPT-5.2 Mini after a few interactions.
LLMs on device is the future. It's more secure and solves the problem of too much demand for inference compared to data center supply, it also would use less electricity. It's just a matter of getting the performance good enough. Most users don't need frontier model performance.
For example, last week I built a real-time voice AI running locally on iPhone 15.
One use case is for people learning speaking english. The STT is quite good and the small LLM is enough for basic conversation.