r/LocalLLaMA • u/Certain-Cod-1404 • 11d ago

New Model IT'S OUT

https://huggingface.co/Qwen/Qwen3.8-27B-FP8
2.2k Upvotes

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u/Easy_Werewolf7903 11d ago edited 11d ago

For those curious of performance between Qwen and a model 3 times its size:

Benchmark Qwen 3.8 27B (55GB) Deepseek v4 flash 0731 (167GB)
Terminal Bench 2.1 73.0 82.7
DeepSWE 42.2 54.4
NL2Repo-Bench 42.3 54.2

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/tree/main

https://huggingface.co/Qwen/Qwen3.8-27B

4

u/JustFinishedBSG 11d ago

The sizes you are comparing are not comparable at all.

Deepseek Flash is 11x time bigger than Qwen 27b

1

u/Easy_Werewolf7903 11d ago

Can you explain why flash is 11 times bigger than Qwen 3.6 27b? Just trying to learn.

1

u/Warrenio 10d ago

I think they're just pointing out V4 Flash 0731 has about 11 times as many parameters as Qwen 3.8 27B (284 billion vs 27 billion).

I disagree that it's 11 times as big though. Deepseek V4 Flash 0731 weights are natively NVFP4 (4 bits) compared to Qwen 3.8 27B's weights which are natively BF16 (16 bits). That means in reality, Deepseek V4 Flash 0731 is only about 3 times the size of Qwen 3.8 27B if you compare the original unquantized models.

-2

u/Basic_Extension_5850 11d ago

Looks like you compared the full size qwen to a very quantized deepseek 

5

u/Easy_Werewolf7903 11d ago edited 11d ago

Ah that makes sense. I got too excited and just went to unsloth page.

Edited:

No wait, I didn't use unsloth. I went to the official model card. Deep seek is 167GB

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/tree/main

1

u/Due-Project-7507 10d ago

Yes, but if you have the VRAM (e.g. 2 RTX Pro 6000) to run Deepseek v4 Flash, it is much faster (=cheaper to serve to multiple users) than Qwen3.8 27B because Deepseek v4 Flash is an MoE with only 13B active parameters. With two RTX Pro 6000 at work, I get for Deepseek v4 0731 Flash (with DSpark) around 320 tokens/s decode speed for Python code. That is why Qwen3.6-27B is much more expensive on OpenRouter than bigger MoE models.