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Keeps the MoE weights of the first N layers in the CPU
adding a destructor to common_params would cause issues when the object is copied
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Thank you :) |
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Should this options be added to this page, too: |
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thad0ctor
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--cpu-moe to keep all MoE weights in the CPU --n-cpu-moe N to keep the MoE weights of the first N layers in the CPU ggml-org/llama.cpp#15077
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thank you! just got 108T/s with gpt-oss:120b on my dual 5090s with llama-server -hf ggml-org/gpt-oss-120b-GGUF --ctx-size 0 --jinja --flash-attn --n-gpu-layers 99 --reasoning-format none --n-cpu-moe 3 |
blime4
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Feb 5, 2026
* llama : add --n-cpu-moe option Keeps the MoE weights of the first N layers in the CPU
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* llama : add --n-cpu-moe option Keeps the MoE weights of the first N layers in the CPU
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* llama : add --n-cpu-moe option Keeps the MoE weights of the first N layers in the CPU
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* llama : add --n-cpu-moe option Keeps the MoE weights of the first N layers in the CPU
MrLordCat
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Jul 16, 2026
* llama : add --n-cpu-moe option Keeps the MoE weights of the first N layers in the CPU
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* llama : add --n-cpu-moe option Keeps the MoE weights of the first N layers in the CPU
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* llama : add --n-cpu-moe option Keeps the MoE weights of the first N layers in the CPU
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Following @jacekpoplawski suggestion in #14992, adds an option to keeps the MoE weights of the first N layers in the CPU. You can use:
--cpu-moeto keep all MoE weights in the CPU--n-cpu-moe Nto keep the MoE weights of the first N layers in the CPUThe goal is to avoid having to write complex regular expressions when trying to optimize the number of MoE layers to keep in the CPU.
These options work by adding the necessary tensor overrides. If you use
--override-tensorbefore these options, your overrides will take priority.