Do you know if it's doing what was described earlier, when I run it with all layers on GPU - paging an expert in every time the expert changes? Each expert is only 5.1B parameters.
It makes absolutely no sense to do what OP described. The decode stage is bottlenecked on memory bandwidth. Once you pull the weights from system RAM, your work is almost done. To then gigabytes of weights PER TOKEN over PCIE to do some trivial computation on the GPU is crazy.
What actually happens is you run some or all of the MoE layers on the CPU from system RAM. This can be tolerable for smaller MoE models, but keeping it all on the GPU will still be 5-10x faster.
I'm guessing lmstudio gracefully falls back to running _soemthing_ on the CPU. Hopefully you are running only MoE on the CPU. I've only ever used llama.cpp.
I tried a few things and checked CPU usage in Task Manager to see how much work the CPU is doing.
KV Cache in GPU and 36/36 layers in GPU: CPU usage under 3%.
KV Cache in GPU and 35/36 layers in GPU: CPU usage at 35%.
KV Cache moved to CPU and 36/36 layers in GPU: CPU usage at 34%.
I believe you that it doesn't make sense to do it this way, it is slower, but it doesn't appear to be doing much of anything on the CPU.
You say gigabytes of weights PER TOKEN, is that true? I think an expert is about 2 GB, so a new expert is 2 GB, sure - but I might have all the experts for the token already in memory, no?
Do you know if it's doing what was described earlier, when I run it with all layers on GPU - paging an expert in every time the expert changes? Each expert is only 5.1B parameters.
^ Er, misspoke, each expert is at most .9 B parameters there's 128 experts. 5.1 B is number of active parameters (4 experts + some other parameters).
It makes absolutely no sense to do what OP described. The decode stage is bottlenecked on memory bandwidth. Once you pull the weights from system RAM, your work is almost done. To then gigabytes of weights PER TOKEN over PCIE to do some trivial computation on the GPU is crazy.
What actually happens is you run some or all of the MoE layers on the CPU from system RAM. This can be tolerable for smaller MoE models, but keeping it all on the GPU will still be 5-10x faster.
I'm guessing lmstudio gracefully falls back to running _soemthing_ on the CPU. Hopefully you are running only MoE on the CPU. I've only ever used llama.cpp.
I tried a few things and checked CPU usage in Task Manager to see how much work the CPU is doing.
KV Cache in GPU and 36/36 layers in GPU: CPU usage under 3%.
KV Cache in GPU and 35/36 layers in GPU: CPU usage at 35%.
KV Cache moved to CPU and 36/36 layers in GPU: CPU usage at 34%.
I believe you that it doesn't make sense to do it this way, it is slower, but it doesn't appear to be doing much of anything on the CPU.
You say gigabytes of weights PER TOKEN, is that true? I think an expert is about 2 GB, so a new expert is 2 GB, sure - but I might have all the experts for the token already in memory, no?
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