Basically the title.

For me, a dense model around 20b, with an additional engram table of 10-20b, or a sparse MoE around 80b would be amazing.

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[–] 1 point 2 days ago

30-35B or just under seems perfect to me.

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  • [–] 1 point 3 days ago

    Something that would make good use of a 128GB spark. GPT-OSS-120b is good but shows its age

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  • [–] 10 points 6 days ago (1 child)

    Flash next 125b is genuinely amazing, I hope to see 4.0 of the same size.

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  • [–] 12 points 6 days ago (1 child)

    Another 35B-A3B MoE or similar would be nice...

    FYI, Yandex released an 80B MoE base model (i.e. not instruction tuned!) recently: https://huggingface.co/yandex/AliceAI-Foundation-80B-A3B-Base -- haven't experimented with it personally though.

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  • [–] 12 points 6 days ago (2 children)

    gemma 26b-a4b was fantastic, anything in that area is ideal

    running my tiny rx6600 8gb vram

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  • [–] 1 point 6 days ago

    I have an M4 Pro 24GB and I still use Gemma 26B-A4B as my primary model. Beats every other model I’ve ever tried locally on most stuff except coding and tool use, so a Qwen 4 at this size would be super interesting

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  • [–] 2 points 5 days ago (1 child)

    They've changed the license with Qwen 4, starting with 3.8-next. So I probably won't be using any further releases

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  • [–] 5 points 6 days ago

    32GB RAM and 8GB VRAM here, so 35b a3b is about my maximum, maybe with added n-grams

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  • [–] 4 points 6 days ago (3 children)

    27B dense was a good size for 3.8, but I'm guessing they're going to add a few B parameters in order to make it noticeably better.

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  • [–] 1 point 5 days ago*

    If they can make the KV cache size scale more efficiently with many tokens, like DeepSeek 4.1 Flash fitting 1 million tokens in 890MB, it might give them enough room to add a couple billion parameters of weights and still fit well in 32GB GPUs

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