2. Yes, and my MBP M1 Pro can run quantized 34b models. My point was that when you do MoE, memory requirements suddenly become too challenging. A 7b Q8 is roughly 7GB (7b parameters × 8 bits each). But 8x of that would be 56GB, and all of that must be in memory to run.

Its just that each bit picks up different "parts" of the training more strongly, which can be selectively picked at runtime. This is actually kinda analogous to animals, which dont fire every single neuron so frequently like monolithic models do.

The tradeoff, at equivalent quality, is essentially increased VRAM usage for faster, more splittable inference and training, though the exact balance of this tradeoff is an excellent question.