alt Hacker NewsAlready on HuggingFace: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash
The bad news is that the original v4 flash was 284B, which was large but still somewhat reasonable for running locally. This one is 552B so almost twice that, so the huge gains in benchmark scores make sense - it's not really flash anymore, imo.
I've no idea about actual performance vs benchmaxxing, though deepseek was fairly trustworthy as far as Chinese models go. If that holds (and if it doesn't think forever, as deepseek 4 sometimes did) it's probably the newest king of the hill amongst open weights models.
It does include vision, and they do something funky with KV cache so it's very efficient: "[...] these designs reduce the global KV cache footprint to 890 bytes per token — roughly 1/4 of DeepSeek-V4-Flash". I do appreciate the high focus on efficiency, but at this point we sure could use a flash-flash version.
@edit: I couldn't make sense what the actual parameter count is, with the addition of Engram memory. To my understanding the 4.1 flash is 552B parameters you want in vram or ram, out of which ~16B is active (8B for prefill). It also includes additional 196B Engram memory which you can put on an SSD. I think.
Assuming that's correct 256 GB memory is insufficient to even load the model at q4 - you'd be 1GB short, assuming you can fill it to 100% (so no mac). You'd also want some for kv cache of course. A 256 GB desktop with some extra VRAM from GPU could run it, but normal consumer boards get real slow once you fill 4 slots so you'll probably want quad channel which is Threadripper or above territory.
Replies
>This one is 552B so almost twice that, so the huge gains in benchmark scores make sense - it's not really flash anymore, imo.
It uses fewer active parameters, though. (8B or 14B instead of always 13B)
So ... flash indeed.
> It also includes additional 196B Engram memory which you can put on an SSD. I think
You can put Qwen 3.8 Flash Next engram on SSD, but prompt processing takes a good hit. On my mac studio, I get 300 pp and 33 tg with SSD offload, versus 550/40 with everything in RAM.
I will be very happy if 300 pp is achievable with this model though.
V4 Flash also was released as mostly FP4, but this one is FP8 (?). 160GB vs 510GB.
Original Flash good fit for dual Spark / Strix Halo machines. This one would require third party quants and even then 4 machines.
Edit: Most of added weights/size are Engrams?
> Overall, DeepSeek-V4.1-Flash has 552B backbone parameters and 196B Engram parameters, activating 8B parameters per token during prefill and 16B during decode.
Those can stay on SSD. So I guess / it possible, that non-engram portion is still FP4 of ~same size! Need to read tech report.
it is a way bigger model with extra 200B engram so of course the score improves.
can't wait for deepseek v4.1 pro
Actually this ought to run quite well with SSD streaming. The MoE expert sparsity seems to be similar to DSv4 Pro (hence exceptionally sparse) but with far fewer total and activated params. The added engram params can reside on disk as well (similar to Qwen Flash-Next), the additional load on storage performance will be quite negligible for typical scenarios.
By reducing per-session KV cache requirements even further compared to DSv4 Flash, this model likely opens up near-frontier model inference (in slow, unattended scenarios) even on low-end consumer hardware, as long as it has enough fast storage to host the model weights. This will be extremely exciting.