I wonder how much MoE will disrupt this. qwen3:30b-a3b is pretty good even on pure CPU, but a lot smarter than a 3B parameter model. If the CPU-GPU bottleneck isn't too tight, a large model might be able to sustainably cache the currently active experts in GPU RAM.
The recent qwen3 models run fine on CPU + GPU, and so does gpt-oss. LM Studio and Ollama are turnkey solutions where the user has to know nothing about memory management. But finding benchmarks for these hybrid setups is astonishingly difficult.
I keep thinking that the bottleneck has to be CPU RAM, and for a large model the difference would be minor. For example with an 100 GByte model such as quantised gpt-oss-120B, I imagine that going from 10G to 24G would scale up my tk/s like 1/90 -> 1/76, so 20% advantage? But I can't find much on the high-level scaling math. People seem to either create calculators that oversimplify, or they seem too deep into the weeds.
