Claude cooked on the code, but I wrote this post myself, caveman style. I wanted to play with Qwen3.5-122B, but I don't have a unified memory system to work with, and 15 tok/s was rough. 23 tok/s is still rough but honestly noticeably faster when streaming responses.
Tl;dr:
- We keep track of which experts get routed to most frequently for the past N tokens. We make a bet that the processing speed-up from loading these frequently routed-to experts into VRAM will outweigh the latency penalty for transferring expert tensors from system RAM (cold) into VRAM (hot). Rinse and repeat every N tokens.
First off, results:
- vs. all-CPU experts baseline:
- +44.8% token generation (15.65 tok/s -> 22.67 tok/s)
- no prompt processing regression
- vs. layer-based offload at equivalent VRAM commitment:
- +26.8% token generation (17.87 tok/s -> 22.67 tok/s)
- very slightly slower prompt processing
Baseline: All experts offloaded to CPU (LLAMA_ARG_OVERRIDE_TENSOR=exps=CPU)
- Prompt processing (tok/s, n=2928): 514.93, 534.64, 531.26
- Token generation (tok/s, n=~300): 15.60, 15.67, 15.69
Partial Layer Offload (22.6 GB VRAM used): 8 layers loaded on GPU (LLAMA_ARG_N_CPU_MOE = 40)
- Prompt processing (tok/s, n=2929): 556.42, 581.73, 618.08
- Token generation (tok/s, n=~300): 17.93, 17.81, 17.87
Hot expert cache (22.2 GB VRAM used): 44 expert slots in VRAM cache (LLAMA_ARG_MOE_HOT_K = 44, LLAMA_ARG_MOE_HOT_REBALANCE_INTERVAL=60, LLAMA_MOE_HOT_PP_BYPASS_N_TOKENS=64)
- Prompt processing (tok/s, n=2929): 557.18, 542.76, 546.77
- Token generation (tok/s, n=~300): 22.26, 22.97, 22.77
Setup:
- RTX 4090 24GB + Ryzen 9 7950X 96GB
- bartowski's Qwen3.5-122B-A10B Q4_K_L + bf16 vision mmproj
- KV Cache 131K tokens @ Q8_0/Q8_0
- For prompt processing, ubatch=3072 & batch=3072
Repo here with more details (code only for now, no binaries, still cooking): https://github.com/ParmesanParty/llama.cpp
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