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.

▲ johnnyApplePRNG 4 hours ago | parent | next [-]

>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.

▲

tarruda an hour ago | parent [-]

200B of those 552B is PLE, which works more like a database that is read for each token, thus can be offloaded to a fast SSD.

	▲	azath92 6 minutes ago \| parent \| next [-]
		Id love an ELI5 for PLE. Im trying to work it into my back of the napikin math for compute vs memory bandwidth limitations on tok/s in PP vs TG work. My attempt at a simplification of this article on it https://sebastianraschka.com/llm-architecture-gallery/per-la... into a couple of sentences is that they are linear embeddings of the input token space projected per layer, which are then gated by the transformer outputs per layer. This would mean that the only one set of weights for the ple path needs to be pumped across the memory bandwidth as they are the same linear weights for all layers? Sheit, maybe im trying to simplify something that i need to look at in detail. but id love to leverage others understanding if possible
	▲	asamoahf 14 minutes ago \| parent \| prev [-]
		[flagged]

▲ tarruda an hour ago | parent | prev | next [-]

> 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.

	▲	hadlock 11 minutes ago \| parent [-]
		You can warm cache regularly used engram/n-gram if you're willing to merge PRs into a personal branch and build it yourself. I was trying this with qwen 3.8 flash next and the n-gram to get it to fit on my very average gaming desktop (it worked)

▲ petu 4 hours ago | parent | prev | next [-]

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.

▲ petu 3 hours ago | parent [-]

It's larger than previous V4 Flash.

  552B in ~FP4, 306GB.   
  196B of FP8 Engrams, another 204GB, not necessary to keep in RAM.  
  KV cache sees another 4x size reduction, just 900MB for 1M.

So 384GB needed for a chance of achieving useful speeds. Three Sparks or quad RTX PRO 6000.

	▲	npodbielski 2 hours ago \| parent [-]
		Or two gorgon halos?

▲ npn 4 hours ago | parent | prev [-]

it is a way bigger model with extra 200B engram so of course the score improves.

can't wait for deepseek v4.1 pro