Or just realize that nobody fucking likes LLMs as much as the Captains of Industry want us to believe, and that the true power of this technical domain lies in more targeted and bespoke ML model generation and usage.
ML is good and enables - and has enabled - some genuine generational leaps in science and technology. But LLMs are such a fucking waste of the technology’s potential. Not to mention, in extremely irritated that (largely due to Nvidia cornering the market) everyone is super gung-ho about a digital approach which amounts to brute-forcing neural nets digitally with shitloads of memory and highly-parallel compute, when it’s obvious to anyone with more than a passing familiarity with electrical engineering that an analog approach is going to be FAR more efficient in terms of resource and energy usage.
Maybe optimize the LLM to run on less compute?
Or just realize that nobody fucking likes LLMs as much as the Captains of Industry want us to believe, and that the true power of this technical domain lies in more targeted and bespoke ML model generation and usage.
ML is good and enables - and has enabled - some genuine generational leaps in science and technology. But LLMs are such a fucking waste of the technology’s potential. Not to mention, in extremely irritated that (largely due to Nvidia cornering the market) everyone is super gung-ho about a digital approach which amounts to brute-forcing neural nets digitally with shitloads of memory and highly-parallel compute, when it’s obvious to anyone with more than a passing familiarity with electrical engineering that an analog approach is going to be FAR more efficient in terms of resource and energy usage.
But everything’s computer!
Don’t worry, Chinese LLM vendors without access to the newest hardware will take care of that.
based,hopefully they use this to optimize local LLMS so i have higher tokens per second.