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Comment by rgbrgb | Hacker News Reader

yep, title is inaccurate. it's a distill into Qwen 7B DeepSeek-R1-Distill-Qwen-7B-Q4_K_M.gguf

The document contains multiple sections. The initial section does reference DeepSeek-R1-Distill-Qwen-7B-Q4_K_M.gguf as the example model but if you continue reading further you'll see a section referencing running DeepSeek-R1-Q4_K_M.gguf plus claims several other variations have been tested.

It's a bit less exciting when you see they're just talking about offloading parts from the large amount of DRAM.

So you thought there was some magical way to get >600B parameters in a couple of GPUs?

Also, LM studio lets you run smaller models in front of larger ones, so I could see having a few GPU in front really speeding up using R1 for inference.

The MoE architecture allows you to keep the entire active model on a single GPU. If two consecutive tokens use the same export then the second token is going to be much faster.

What is the probability of that happening?

DeepSeek V3/R1 uses 8 routed experts out of 256, so not all as often as one would like. That said, having even just a single GPU will greatly speed up prompt processing which is worth it even if the inference speed was the same.

Ktransformers has a document about using CPU + a single 4090D to reach decent tokens/s but I'm not sure how much of the perf is due to the 4090D vs other optimizations/changes for the CPU side https://github.com/kvcache-ai/ktransformers/blob/main/doc/en... The final step of going to 6 experts instead of 8 feels like cheating (not a lossless optimization).

where does 256 come from? it's repeated in here and elsewhere that a single expert is 37B sized, so you'd have to have way more than "several hundred billion parameters", to hold 256 of those? Maybe i don't understand the architecture, but if that's the case, then everyone repeating 37B doesn't, either.

I think this diagram from the DeepSeekMoE paper explains it the clearest: https://i.imgur.com/CRKttob.png The one on the right is how the feed forward layers of DeepSeek V3/R1 work, blue and green are experts, and everything in that right section is what counts as "active parameters".

K (K=8 for these models, but you can customize that if you want) experts of 256 per layer are activated at a time. The 256 comes from the model file, it's just how many they chose to build it with. In these models there is also 1 shared expert which is always active in the layer. The router picks which k routed experts to use each forward pass and then a gating mechanism combines the outputs. If you sum the 1 shared expert + K routed experts + router + output networks you end up with 37 B parameters active for each feed forward layer pass. The individual experts are therefore much smaller than the total (probably something like 4 B parameters each? I've never really checked that directly).

Or, for the short answer: "37 B is the active parameters of 9 experts + 'overhead', not the parameters of a single expert".

I understand all that, I am talking about a separate feature that is possibly backported or from llama.cpp. Where you have a small model that runs first and that is checked by a large model. I've seen 30%+ speedups using like 1.5B in front of a 15B for example.

Two GPUs or more mean you can start to "keep" one or more of the experts hot on a GPU as well.

I had also initially assumed the title was supposed to reference something new about running a distilled variant as well. When I finished reading through and found out the news was just that you can also do this sort of "split" setup with Intel gear too it removed any further hope of excitement.

DeepSeek employs multi-token prediction which enables self-speculative decoding without needing to employ a separate draft model. Or at least that's what I understood the value of multi-token prediction to be.

See this section https://github.com/intel/ipex-llm/blob/main/docs/mddocs/Quic...

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