Since implementation of the --fit parameter and its relatives, and --fit on becoming the default, llama.cpp intelligently decides what to offload. For me, it made --n-cpu-moe obsolete.
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-ctx and how much space the algorithm should leave unallocated (even on a per-GPU basis) with --fit-target.
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.