A good rule of thumb is to think that one param is one unit of storage. The "default" unit of storage these days is bf16 (i.e. 16 bits for 1 weight). So for a 80B model that'll be ~160GB of weights. Then you have quantisation, usually in 8bit and 4bit. That means each weight is "stored" in 8bits or 4bits. So for a 80B model that'll be ~80GB in fp8 and ~40GB in fp4/int4.

But in practice you need a bit more than that. You also need some space for context, and then for kv cache, potentially a model graph, etc.

So you'll see in practice that you need 20-50% more RAM than this rule of thumb.

For this model, you'll need anywhere from 50GB (tight) to 200GB (full) RAM. But it also depends how you run it. With MoE models, you can selectively load some experts (parts of the model) in VRAM, while offloading some in RAM. Or you could run it fully on CPU+RAM, since the active parameters are low - 3B. This should work pretty well even on older systems (DDR4).

Thats not a meaningful question. Models can be quantized to fit into much smaller memory requirements, and not all MoE layers (in MoE models) have to be offloaded to VRAM to maintain performance.