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Comment by IceWreck | Hacker News Reader

It does if you use an inference engine where you can offload some of the experts from VRAM to CPU RAM. That means I can fit a 35 billion param MoE in let's say 12 GB VRAM GPU + 16 gigs of memory.

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With that you are taking a significant performance penalty and become severely I/O bottlenecked. I've been able to stream Qwen3.5-397B-A17B from my M5 Max (12 GB/s SSD Read) using the Flash MoE technique at the brisk pace of 10 tokens per second. As tokens are generated different experts need to be consulted resulting in a lot of I/O churn. So while feasible it's only great for batch jobs not interactive usage.

> So while feasible it's only great for batch jobs not interactive usage.

I mean yeah true but depends on how big the model is. The example I gave (Qwen 3.5 35BA3B) was fitting a 35B Q4 K_M (say 20 GB in size) model in 12 GB VRAM. With a 4070Ti + high speed 32 GB DDR5 ram you can easily get 700 token/sec prompt processing and 55-60 token/sec generation which is quite fast.

On the other hand if I try to fit a 120B model in 96 GB of DDR5 + the same 12 GB VRAM I get 2-5 token/sec generation.

Your 120B model likely has way more active parameters, so it can probably only fit a few shared layers in the VRAM for your dGPU. You might be better off running that model on a unified memory platform, slower VRAM but a lot more of it.

Yep, I understand I was giving an example to the person I was replying to.

10 tok/s is quite fine for chatting, though less so for interaction with agentic workloads. So the technique itself is still worthwhile for running a huge model locally.

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