Very rough (!) napkin math: for a q8 model (almost lossless) you have parameters = VRAM requirement. For q4 with some performance loss it's roughly half. Then you add a little bit for the context window and overhead. So a 32B model q4 should run comfortably on 20-24 GB.

Again, very rough numbers, there's calculators online.

The ultra-simplified napkin math is 1 GB (V)RAM per 1 billion parameters, at a 4-5 bit-per-weight quantization. This usually gives most of the performance of the full size model and leaves a little bit of room for context, although not necessarily the full supported size.

Wondering if I'll get corrected, but my _napkin math_ is looking at the model download size — I estimate it needs at least this amount of vram/ram, and usually the difference in size between various models is large enough not to worry if the real requirements are size +5% or 10% or 15%. LM studio also shows you which models your machine should handle

The absolutely dumbest way is to compare the number of parameters with your bytes of RAM. If you have 2 or more bytes of RAM for every parameter you can generally run the model easily (eg 3B model with 8GB of RAM). 1 byte per parameter and it is still possible, but starts to get tricky.

Of course, there are lots of factors that can change the RAM usage: quantization, context size, KV cache. And this says nothing about whether the model will respond quickly enough to be pleasant to use.