I don't understand the logic behind model sizes and quantization.

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.

It really depends. It used to be easier to have a rule of thumb, but now it's not clear anymore. Now there are a lot of things to consider, such as a model's kv efficiency (how much context you can fit), MoE v. dense, QAT or not (Quant aware training) and so on.

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.

Standard models are designed to quantize down to 4-bits relatively well.

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.

Usually 4-bit 200B model is better than 8-bit 90B. But if you go below 4 bits, I am not sure what is better.

There's no rhyme or reason to it. Quants aren't benchmarked much. Generally 4bit better than smaller model 8bit