I think that’s too simplified. The best LLMs will still frequently make mistakes. Meta is advertising a HumanEval score of 67.8%. In a third of cases, the code

I think that’s too simplified. The best LLMs will still frequently make mistakes. Meta is advertising a HumanEval score of 67.8%. In a third of cases, the code generated still doesn’t satisfactorily solve the problem in that automated benchmark. The additional errors that quantization would introduce would only be a very small percentage of the overall errors, making the quantized and unquantized models practically indistinguishable to a human observer. Beyond that, lower accuracy can manifest in many ways, and “do the opposite” seems unlikely to be the most common way. There might be a dozen correct ways to solve a problem. The quantized model might choose a different path that still turns out to work, it’s just not exactly the same path. As someone else pointed out, FLAC is objectively more accurate than mp3, but how many people can really tell? Is it worth 3x the data to store/stream music in FLAC? The quantized model would run at probably 4x the speed of the unquantized model, assuming you had enough memory to choose between them. Is speed worth nothing? If I have to wait all day for the LLM to respond, I can probably do the work faster myself without its help. Is being able to fit the model onto the hardware you have worth nothing? In essence, quantization here is a 95% “accurate” implementation of a 67% accurate model, which yields a 300% increase in speed while using just 25% of the RAM. All numbers are approximate, even the HumanEval benchmark should be taken with a large grain of salt. If you have a very opulent computational experience, you can enjoy the luxury of the full 67.8% accurate model, but that just feels both wasteful and like a bad user experience.