Is that how MoEs work? I though that an important constraint for MoEs is that experts need to be uniformly used to make sure they can be used effectively. If there is a 'common subset' that, if anything, sounds like a symptom of undertraining (i.e. the same trick will not work as well for Deepseek V4.1).
Also, even if your MoE hitrate is 90%, you still spend half your time waiting for the SSD, giving similar total speed to a 27B model!
Finally, it looks like Deepseek V4 is pretty much only runnable with antirez's ds4, and SSD streaming only works with Metal; but I would like to try what you say with llama.cpp which uses mmap to also potentially do SSD streaming. (I can maybe try the large Qwen3.5 MoEs?)
> as context length increases
What kind of context length do you consider reasonable, though? From what I know, all models (even frontier ones) start degrading once you pass a few hundred thousand tokens. So realistically, limiting context size might even improve quality, especially if you use token-efficient harnesses.
> Or you could fit the dense portion of a much more capable model and still take advantage of that hardware.
Your point about consumer hardware was that it would be "borderline unusable" when running Qwen 3.6 27B. However, you need much less hardware to run a 27B than DSv4 Flash. In addition, you can do the same 'trick' with low-end GPUs and small MoEs: my desktop with 32 GB DDR4-3200 and an RTX 2070 8GB can run the ~30B class MoEs at 20-30 tokens per second and similar speeds to my laptop.
For any given workload/session? Empirically, yes, that's what has been found across different models. There's quite a bit of predictability that makes caching helpful.
> Also, even if your MoE hitrate is 90%, you still spend half your time waiting for the SSD, giving similar total speed to a 27B model!
There are ways of masking some of that latency, though it requires some architecture-specific cleverness which is less directly applicable to a generic engine like llama.cpp.
> Finally, it looks like Deepseek V4 is pretty much only runnable with antirez's ds4, and SSD streaming only works with Metal
The llama.cpp folks are working on adding support, and the ds4 project is working on CUDA support for streaming inference, targeting the DGX Spark.
> From what I know, all models (even frontier ones) start degrading once you pass a few hundred thousand tokens.
DeepSeek V4 seems to do quite well on recall tasks even with large context. That's one plausible benefit of its compressed attention mechanism, compared to earlier models. Some degradation will likely still be there, but it's not necessarily obvious.
As for why people are calling Qwen 27B "borderline unusable" that may have to do with it being a dense model which makes for an increased compute intensity and pushes users towards discrete GPU platforms, since those tend to have the most compute overall as far as consumer hardware is concerned. I might agree that Qwen 27B is quite ideally tailored towards these platforms, but that does come with some limitations.