2 ms·

Are there any benchmarks (or even vibes!) about the token/second one can expect with this strategy?

• zozbot234 8mo ago
  No real fixed benchmarks AIUI since performance will then depend on how much extra RAM you have (which in turn depends on what queries you're making, how much context you're using etc.) and how high-performance your storage is. Given enough RAM, you aren't really losing any performance because the OS is caching everything for you. (But then even placing inactive experts in system RAM is controversial: you're leaving perf on the table compared to having them all in VRAM!)
• cpburns2009 8mo ago
  In my short testing on a different MoE model, it does not perform well. I tried running Kimi-K2-Thinking-GGUF with the smallest unsloth quantization (UD-TQ1_0, 247 GB), and it ran at 0.1 tps. According to its guide, you should expect 5 tps if the whole model can fit into RAM+VRAM, but if mmap has to be used, then expect less than 1 tps which matches my test. This was on a Ryzen AI Max+ 395 using ~100 GB VRAM.
  • zardo 8mo ago
    That's NVME storage in your test?
    • cpburns2009 8mo ago
      Yes, a WD_BLACK 4TB SN850X NVMe.
  • zozbot234 8mo ago
    Running a 247GB model reliably on 100GB VRAM total is a very impressive outcome no matter what the performance. That size of model is one where sensible people will recommend at least 4x the VRAM amount compared to what you were testing with - at that point, the total bandwidth to your storage becomes the bottleneck. Try running models that are just slightly bigger than the amount of VRAM you're using and these tricks become quite essential, for a significantly more manageable hit on performance.