30-35B or just under seems perfect to me.
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Something that would make good use of a 128GB spark. GPT-OSS-120b is good but shows its age
Flash next 125b is genuinely amazing, I hope to see 4.0 of the same size.
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.
gemma 26b-a4b was fantastic, anything in that area is ideal
running my tiny rx6600 8gb vram
Oh hell yeah
I'm on my 1070 8gb copium so we're twinsies
Maybe they release a huge moe model with non-uniform quants and then we can get even more quality on those cute little cards
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
They've changed the license with Qwen 4, starting with 3.8-next. So I probably won't be using any further releases
32GB RAM and 8GB VRAM here, so 35b a3b is about my maximum, maybe with added n-grams
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.
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
Not necessarly, I'd say. We've seen many releases with multiple sizes and the 27B class has been around since at least the Gemma3 times.
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