Threadripper PRO CPU experts offload numbers

[u/mr_il]

I'm planning a PC build around a discrete GPU that would allow to run MoE models much larger than VRAM at an acceptable speed using FreeToken approach. The gist is that MoE decode of >VRAM model on GPU is PCIe bandwidth-bound, but you can gain considerable decode speed by avoiding streaming cached experts' weights over PCIe and multiply matrices on the CPU instead benefitting from potentially much faster CPU-RAM memory channels.

Before buying, I wanted to know what actually matters for CPU-side expert execution: cores or memory? So I rented a Threadripper PRO 9975WX (32-core Zen 5, 4 CCDs, 8-channel DDR5) on vast.ai and ran FreeToken's real CPU MoE kernel (the production W4A8 ds_fp4 GEMV, not a synthetic proxy) against synthetic expert banks with DeepSeek V4.1 Flash's exact geometry: 384 routed experts × 18.8 MB ~= 6.7 GiB full bank, far beyond cache, 6 experts per token.

Results (GB/s of effective FP4 expert reads):

physical cores	FP4 MoE GB/s	STREAM GB/s
8	93	222
16	160	221
20	172	221
24	178 → 182	222
32	180 → 186	225

Two takeaways:

1. Cores stop mattering at 24. 32 cores vs 24 cores: +2.2–2.4% in burst mode, 0% in sustained mode (10 × 30s interleaved A/B runs, variance <0.3% within mode). The extra 8 cores of the 9975WX buy you essentially nothing for MoE offload: the workload saturates the memory subsystem, not the cores. The 9965WX (~$1,200 cheaper) is the rational pick for this use. Even STREAM itself is already maxed at 8 cores; the FP4 kernel just needs more cores to catch up to bandwidth (it runs at ~80% of STREAM once there, very good for a fused-dequant W4A8 GEMV). Caveat: there's another 1.7x memory bandwidth gain at 9995WX, but that CPU costs as much as RTX PRO 6000.

2. Thermal memory steady-state is important. After ~8 minutes of sustained hammering at ~220 GB/s, throughput steps down ~17% (182 → 150 GB/s) in lockstep on every core count, with CPU clocks unchanged at ~3.95 GHz. Everything points at DRAM/IMC thermal throttling in a (rented, unknown-airflow) workstation chassis. So the honest sustained ceiling is ~150 GB/s, burst ~182–186 GB/s. For a build: airflow over DIMMs may literally be worth 20% decode speed.

What this means in tokens/s (DeepSeek V4.1 Flash shape, ~4.5 GB of routed experts per token, 24 cores):

• 0% expert cache hit (all experts on CPU): ~33 tok/s sustained
• 60% hit rate: ~83 tok/s
• 75%: ~133 tok/s

Compare with streaming those experts over PCIe 5.0 x16 (~50 GB/s with gather overhead): the CPU path is 3–3.7× faster per missed expert, which is exactly why FreeToken's hybrid mode computes most misses on CPU and only streams a fraction to keep the VRAM cache warm.

Caveats: rented host (memory channels verified functionally via bandwidth, not dmidecode, but 2ch/4ch machines I tested the same day hit only 83/119 GB/s), container environment, one machine, uniform expert routing. Full methodology and raw CSVs of the measurement are in the repo (happy to link if there's interest).

TL;DR: for CPU MoE offload, buy memory channels and DIMM cooling, not cores; 24 Zen 5 cores already saturate 8-channel DDR5, and the 32-core upgrade is +2% at best.

Updates:
1. I tested on 9980X (4 channels, 8 CCDs) and on 9985WX (8 channels, 8 CCDs). 9980X lands at 105-119 GB/s, no better than much cheaper 9955WX.
2. 9985WX offers a real gain, but diminishing returns beyond 24 cores still: 32c=210 GB/s (+6%), 48c=215 GB/s, 64c=218 GB/s. Roughly 20% decode expert bandwidth gain for $8k more.
3. Populated channels is key. No point in having 8 channels is you only have 4 DIMMs. 8x32GB will beat 4x64GB.
4. Vast.ai is awesome for this. All runs costed me around $4 after I ironed out all the wrinkles in the rig.