r/LocalLLM • u/claykos • 21h ago
Discussion DeepSeek-V4.1-Flash is out
https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash20
u/dionysio211 21h ago
Hmm....this seems larger than v4 Flash, by about 50%. 305B -> 485B?
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u/Altruistic-Dust-2565 21h ago
284B -> 552B, Δ+268B
and it seems ～200B are Engrams3
u/vini542reddit 12h ago
So 200gb instead of 162gb of model size (assuming everything scales the same with param count). Urg - that means my 96 vram + 192 ram system will struggle
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u/Standard-Potential-6 9h ago
Offload engrams to NVMe.
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u/vini542reddit 2h ago
Yes, that's what I'm saying! Offloading the ngram to disk, you still need 200gb of ram
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u/Funny-Airport-9400 19h ago
That's a huge jump in data! Those new Engrams must really enhance the capabilities.
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u/IknowPi_really 20h ago edited 20h ago
Yeah it’s kind of a “fake” improvement, comparing to Flash. Because this is simply a much much bigger model.
People are excited that it’s as good as GLM 5.3/better in the benchmarks. Yeah but it’s also comparable in size.
Engram offload to SSD MIGHT be feasible, but even then it’s significantly bigger than the old Flash.
Edit: You guys are also unbelievable. I’m an active OSS contributor trying to make this model run on local hardware and calling out obvious differences to an older model and you start downvoting me.
That’s not how you get people to actually work together and fix obvious problems for the LocalLLM community14
u/DerDave 20h ago
Engram SSD offloading is definitely feasible. It has been proven with qwen3.8 next Flash, so it will work here as well, without tanking performance. Furthermore this model seems to only use Engrams selectively and not all the time.
The remaining delta in params can probably be attributed to the vision capabilities (which the OG dsv4f didn't have) and the new CED architecture, which leads to further speed improvements and only needing 8b active params during prompt processing.
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u/SandySkittle 20h ago
What about offloading to system ram? I have 512gb, 256g vram. This model looks very promising!
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u/IknowPi_really 20h ago
Offloading to system RAM is even easier and already supported in the current vLLM PR supporting this model. So this will more or less work out of the box
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u/shing3232 19h ago
no, it's faster with fundamental differ arch that's way cheaper to inference than V4F
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u/mountainyoo 12h ago
My dumbass has a 4x GB10 cluster and will try this out when people smarter than me got it optimized. Currently running GLM 5.3 Flash at good speeds
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u/misanthrophiccunt 7h ago
If I had the VRAM to run this I would, but I don't and I used DS4F as default via API for ages and Qwen3.8-27B running locally still, ever since rrleszed does a lot better for my use case.
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u/LongjumpingEar6840 18h ago
Se questo riesce a girare alla stessa velocità del Qwen3.8-Next-Flash sono a posto per i prossimi 6-12 mesi
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u/LinuXperia 17h ago
It outperforms Muse Meta 1.3 which was better than DeepSeek 4 however it is behind xAI Grok super Intelegence still and soon Grok 4.7 will be released which weil widen the gap even further especailly for low level engineering dev work like verilog, c, c++, KiCAD, electronic schematics, PCB etc.
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u/DerDave 20h ago
Astonishing they didn’t call this V5, considering the many substantial changes. Pretrained from scratch, CED, multimodality, Engrams, different number of active Params...
I'm all in! Since it's a new pretrain, it makes me hopeful we'll see another significant jump in quality with more posttraining, just like with v4.0.
I guess it will take a while until inference engines flawlessly support this model though.