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Do you host your own AI?
(aussie.zone)
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this post was submitted on 24 Jun 2026
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CPU offloading is too slow unless you use a hybrid MoE model, with the --n-cpu-moe parameter, specifically.
This only offloads "sparse" parts of the model to the CPU, which take up a lot of RAM but are very compute-lite to run. In practice, thats most of the size of modern MoE LLMs.
Since implementation of the
--fitparameter and its relatives, and--fit onbecoming the default, llama.cpp intelligently decides what to offload. For me, it made--n-cpu-moeobsolete.Mostly, yeah.
Sometimes it’s better to “cut it close,” with (for instance) a 27B model that’s nearly OOMing your VRAM fully offloaded, but you know will be fine in regular use without too many programs open.
In my case, with MiMo 2.5, it fills both my CPU and GPU RAM rather completely, so it’s best to set a static value so I don’t swap CPU RAM, and don’t OOM on the GPU either.
You can control how much context should be fitted with
--fit-ctxand how much space the algorithm should leave unallocated (even on a per-GPU basis) with--fit-target.