native precision or very close to their native precision. So there is a paper showing that if you want to do compression um it's actually most likely better use of resources if you train a ginormous model then you quantize it down um and so there is this like formula where they show um comparing for example a very small model like a 35 billion um you know 35 billion Bflow 16 so 16 bit versus say like um four times bigger like 120 billion at 4bit um and which one's better right so essentially they're the same size in terms of disk case, but which one intelligence- wise is better? And from those experiments, they show that the bigger model quantized to 4bit is actually much better um than a 35 billion 16 bit. So in general, most likely what will happen is we'll get bigger and bigger and bigger and bigger models. Um and you know like okay currently now you know GLM you know 1.5 terabytes you know oh okay it's not that big. Um but what happens if it's 15 terabytes then okay we must do quantization we must do compression this will not fit in anyone's not not even enterprises can now service and so like local models you know you must do compression if they're getting bigger and bigger and bigger um and you know to extract any value out of it and you know okay I guess the DGX station has a lot of memory I guess that's very useful um but you know once we have 10 trillion parameter models that will also not fit and so like you know we need to do compression you I guess quantization for that. Um but in general the small ones are very useful. Um but I think the bigger ones compressed down um is slightly more useful. Um but there's also another trick you can do model routing. For example, for the small ones you can do it for the for example you can use the big ones for planning and then you execution with the small ones. Um but the small ones are still better because they're much faster. So even if you have a big one and you compress it down you'll probably get like five tokens to 10 tokens per second if you don't have enough GPU power. Um and for the small ones you can get 200 tokens per second. Um so I guess like depending on your use case um you also have to consider speed throughput you know what is your GPU