4090 + 5060 Ti + 64GB RAM: 206 t/s on a 35B-A3B, and a 122B at 37 t/s

I've been benchmarking a two-card box for a few weeks and I still can't quite get over some of these numbers, so I'm dumping them here. Box: RTX 4090 (24GB) + RTX 5060 Ti (16GB), i9-13900K, 64GB DDR5. WSL2 with 47GB allocated to the VM, CUDA 12.8 (12.8 specifically,13.1 segfaults llama.cpp's MMQ kernel on Blackwell and silently falls back to cuBLAS, which cost me ~6x on prompt processing before I figured that out). llama.cpp built for 89;120. Everything below is 131K context with q8_0 KV cache, measured on short-code generation. Model Placement MTP on MTP off Qwen3.6-27B dense, Q4_K_XL 4090 only 101–118 t/s 44 t/s Qwen3.6-27B dense, Q6_K_XL both cards, layer split 64 t/s Qwen3.6-35B-A3B, Q4_K_XL 4090 + 6 expert layers spilled 206 t/s 113 t/s Qwen3.5-122B-A10B, IQ3_S 4090 + 5060 Ti + ~15GB in RAM 37–41 t/s 24 t/s The 122B one is the one I keep re-reading. That's a 122-billion-parameter model with 17 of its 49 layers living in system RAM, generating faster than most people's 8B setups. My own napkin estimate before I ran it was 20–30 t/s and I thought I was being optimistic. Scripts and all the raw numbers are in a repo I put up (github.com/04RR/qServer). it's my own, mostly llama.cpp launch flags and regression gates rather than anything clever, but the RESULTS and LEARNINGS files have the full sweeps if anyone wants the ugly details (generated by Claude code ofc) .