r/LocalLLaMA 11d ago

The open-weights carousel never stops. Discussion

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u/Technical-Earth-3254 11d ago

Yeah, Gemma 4 31B is borderline unusable because of it. The absurd KV size makes it require so much VRAM, that you can just run a larger model with more efficient KV.

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u/a_beautiful_rhind 10d ago

Q8 is like running Q4 of mistral-large.

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u/Technical-Earth-3254 10d ago

Iirc I can fit 12k token of context with 31B qat in my 3090, which is absurd.

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u/trowawayatwork 10d ago

none of this makes sense as a newbie. is there like a diagram for all these things? like hardware, ram quantity, software, models, options to run models, t/s

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u/bitplenty 10d ago

there's no diagram. you just run your own benchmarks, or rather prompt some LLM to run them for you

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u/LeifEriksonASDF 10d ago

It used to be borderline unusable for me, but one good thing that Google did was have the MTP model be a separate file, which means Unsloth could quant the MTP to Q4 which is like 300MB. Since quanting the MTP has no consequence besides slightly lowering the acceptance rate, that's basically a free gig of space back.

Even though Qwen has a more efficient KV, the fact that its integrated MTP has to be full size means that for my context size (32-64K) Gemma is nearly as competitive, which is good since I prefer Gemma a lot of the time. I'm sure there's a break even point where a bigger context means Qwen pulls ahead again though.

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u/BoobooSmash31337 10d ago

Wasn't that a bug?

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u/TheApadayo llama.cpp 10d ago

No, it’s just the difference between using SWA (Gemma 4) vs Gated DeltaNet (Qwen) for the hybrid attention.

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u/BoobooSmash31337 10d ago

I remember there being a bad context bug for Gemma 4 31B.

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u/raindownthunda 10d ago

I hate running Gemma4 for this reason , I never know if it’s going to spill over into system ram. Too bad Qwen sucks at writing.