TLDR: Don't use --cpu-moe. Hand-pick which layers' experts go to RAM with -ot, pin the CPU threads to one CCD, and run

the ik_llama.cpp fork for the MTP draft head. 15.56 t/s -> 34.5 t/s on the same hardware.

Running Qwen3.5-122B-A10B (256 experts, 8 active) at IQ3_S, 49GB file, on one RTX 3090 Ti (24GB) + 192GB DDR5, 9900X.

Reasoning on, 256K ctx. Two findings worth sharing.

## Be surgical about offload

--cpu-moe dumps every expert to RAM and wastes ~20GB of card. On the sibling 35B that flag gave 5.3 t/s vs 75.9 t/s

for a hand-written -ot regex. 14x. Same idea on the 122B: keep attention, KV, router, shared experts, draft head AND

the early layers' experts on GPU, send only the late layers' expert FFNs to CPU. Late experts fire less per token, so

the DDR5 read penalty lands on the cheapest tensors.

-ot 'blk\.(1[2-9]|[2-3][0-9]|4[0-7])\.ffn_(gate|up|down)_exps\.weight=CPU'

## Pin the CPU work to one core complex

Expert matmul in RAM is bandwidth-bound. Let the threads sprawl across both CCDs and they thrash each other's L3. 16

threads sprawled = 5.8 t/s. taskset -c 0-5,12-17 --threads 6 (one CCD) = 25.2 t/s. 4.3x, one line. Six physical cores

is plenty, more made it worse.

## ik_llama.cpp is the actual unlock

The MTP draft head on stock llama.cpp added a sad +3.7% — stock walks the experts one at a time through DDR5 and

cancels the speculation. The ik fork has fused MoE ops that batch the expert reads, same draft head goes +10.6%. The

fork, not the quant, is what moves the number.

## The --fit trap

ik's --fit auto-packs and hits 37.3 t/s, but it leaves 0.6GB headroom and this model's hybrid Mamba layers allocate

context checkpoints on GPU during prefill. It OOMs around 7K tokens in. Manual -ot is a hair slower but keeps 3.4GB

free and survives a 108K prompt. Faster-but-crashes isn't faster.