Suppose I have 100GB of unified memory, how should I know which model suits it best? I understand how a 2.4T model wouldn't fit, but I don't understand the impact of quantization and whether I should use a 200G model quantised to fit say 90GB of memory, or a non-quantised 90G model.
What's weird about local LLM models is that closed door improvements in training/RLHF dataset have been so significant that it's rare for larger but older models to make sense - everyone seem to always hard switch to the newest one and report step changes in capabilities(or maybe people running Kimi K2 since release just don't talk about it on the public Internet, giving me that impression).
The old rule of thumb was that a lower quant of a larger model > higher quant of a smaller model. That being said, for some things going lower than fp8 will see a lot of degradation in generation quality. Except if the model comes with QAT 4bit quants. Then there's also nvfp4 w/ calibration data, which also can improve things. So it's really not easy to tell "at a glance" you'd have to test them yourself on your hardware.
Anything below that, and especially 1.58b - is typically complete garbage, and you're much better off running a model 100x smaller at regular precision (compared to one 7x smaller quantized into complete garbage).
If the model was designed specifically to quantize down to 1.58b, then it's different.
AFAIK, there's no large models designed for this yet.
> AFAIK, there's no large models designed for this yet.
Isn't BitNet b1.58 2B4T what you are looking for? (haven't tried it myself though)
No 100B+ param (certainly no 2T+ param) models have been trained natively to quantize down to 1.58b.