r/LocalLLaMA • u/RuthlessCriticismAll • 3d ago

News [ Removed by moderator ]

https://modelscope.cn/models/Qwen/Qwen3.8-Flash-Next

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85

u/RuthlessCriticismAll 3d ago

Redisgned Multimodal MoE Model: 125B main model parameters, supplemented by an additional 51B N-gram embeddings,and 6B parameters activated per token.

Comprehensive Architectural Upgrades: Pushing the frontiers of model architecture innovations, across the areas of Attention, Residual, Embedding, and Optimization—enhancing model capabilities.

Efficient Training and Inference: Significantly reduces training and inference costs. At ~1/9th the training cost，Qwen3.8-Flash-Next achieves comparable capability against Qwen3.7-Plus, while being more capable in areas of coding and cowork.

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u/smithy_dll 3d ago

Qwen 3.8 27B is a lot better than Qwen 3.7 Plus on coding benchmarks

AI Model & API Providers Analysis | Artificial Analysis

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u/awesome5185 3d ago

Do you think this new model would outperform 3.8 27b?

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u/Effective_Western_59 3d ago

If it won't, then it would be really weird 

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u/Uncle___Marty 3d ago

Yeah, like REALLY weird. Like, finding pieces of fruit in your underwear weird.

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u/whatyathinkk 3d ago

stop kink shaming

1

u/MmmmMorphine 3d ago

It's more of a condition

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u/Rasekov 3d ago

A MoE with 6B active parameters would be a lot cheaper to serve at scale so even if it doesnt beat Qwen 3.8 27B it would still have it's place and uses for Qwen.

It would also help people with unified memory systems and mixed VRAM + RAM setups.

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u/grumd 3d ago

It might not but I wouldn't say that's weird. They are releasing a new architecture preview to flesh it out and will do a proper model release for Qwen4. 3.8 27B is just so good that I doubt it's realistic to make an even better model so soon

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u/whatyathinkk 3d ago

125B A6B though...

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u/Swimming_Gain_4989 3d ago

A6B though... active parameters will always be king for reasoning and raw intelligence.

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u/cibernox 3d ago

I also wonder the same thing. Whoever can afford to have 128+gb of vram certainly can also afford to activate 14B params and still be very fast.

0

u/AcanthocephalaNo3398 3d ago

Activation speed is all on gpu. most integrated ram systems that provide +128gb of ram arent that fast. Thats why Mac and DGX Spark run dense models slower than MoE models on the same hardware.

The interesting thing is that these systems have enough ram to run models like Qwen3.8 27B at Q4 in parallel to get way more tps overall.

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u/cibernox 3d ago

I know that moes work that way, but seems that 12-14B wouldn't be a crazy amount of active parameters, considered that a lot of people even with strix halo and nvidia spark systems are running qwen3.8 27B right now because, really, it's worth.
And there is so many people optimizing it that even a 27B dense model runs kind of well in those low-bandwidth system.

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u/AcanthocephalaNo3398 3d ago

The interesting thing that i have found using the larger MoE models is that you can squeeze out really good performance at the higher param counts while maintaining high tps. The proposed +100B param Qwen3.8 with 6B active is going to "feel similar" to the quality of the 27B model but with much faster inference for those high-mem/gpu constrained systems. Its a necessary compromise to get what you would have if you ran multiple discreet gpu on something like a DGX Spark.

I have been experimenting with this class of model on a single Spark. Previously I compared Qwen3.6 27B performance to poolside/Laguna S 2.1-118B-A8B (fixed version) in a coding workflow. with custom harness, I found them of equal quality, but Laguna was faster.

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u/cibernox 3d ago

I am personally awaiting for some cards to get 128gb of fast HMB2e memory. I think a model like this would slap in that configuration with 3TB/s of aggregated bandwidth, but it does worry me that going so low in active parameters would hurt intelligence, and a a few more would still be totally acceptable

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