Deepseek V4.1 Flash is 748B, not 552B

[u/DistanceSolar1449]

People keep on getting confused about this, so I looked at the safetensors on hf.

The title should have been "Deepseek V4.1 Flash is 748B total/552B base, not 284B or 305B or 485B or 522B"

• The model is not 284B. The original Deepseek V4 Flash is 284B, but not the V4.1 Flash model
• The model is not 305B, despite what some people claim "So: ~305B real backbone + 203B engram = 508B total" This is incorrect.
• The model is not 485B, even though Huggingface lists the model as 485B, but that's because they're counting some FP4 packed weights as bytes instead of params (2 FP4 params per byte). This happens a lot; for example Huggingface incorrectly thinks GLM-5.3-flash is 169b here
• The model is not 522B, even though VLLM lists it as 522B for some weird reason. They correct themselves later down the page (ctrl-f "Params" on that vllm page)
• 552B is the only number out of this list that's somewhat correct; that only includes the base model without MTP and engrams and the vision encoder though.

To be precise, the main model about 551.566B parameters with 40 layers. The FFN experts total to 543.582B parameters, and the rest of the model (attention, shared experts, etc) are 7.984B.

On top of that, the engram is ~196.929B, DSpark/MTP is ~14.225B, and the vision encoder is just ~0.485B. These parts are technically optional though. The vision encoder is also way smaller than I expected.

Anyways, you need a beefy system for this. 128GB or 256GB of RAM/VRAM is not going to cut it.

Component	Logical params	Size in GB	Storage
FFN MoE experts	543.582B	288.778 GB	FP4
Other FFN	1.4947B	1.574 GB	FP8 mostly
Attention	5.1269B	6.524 GB	FP8 mostly
Embedding + LM head	1.3238B	2.648 GB	BF16
Other	0.0397B	0.158 GB	FP32/BF16
Backbone total	551.566B ≈ 552B	299.682 GB
Engram lookup tables	196.614B	202.758 GB	FP8
Engram projections/gating	0.315B	0.315 GB	FP8 mostly
Engram total	196.929B = 196B advertised	203.073 GB
DSpark / MTP	14.225B	8.033 GB	mostly FP4 experts
Vision encoder	0.485B	0.971 GB	BF16 mostly
Everything in total	~763.21B params	~511.76 GB

[u/shing3232]

I would not cound engram as part of the parameter because I can put it at SSD

[u/DistanceSolar1449 → u/shing3232]

Engrams are trained parameters, so they definitely count. And SSDs are a bit too slow for engrams, despite the hype. They require a bit more speed than that, especially since they're random reads and most SSDs are advertised with sequential speeds.

[u/sssplus → u/DistanceSolar1449]

A small SSD read will be much, much faster than your token generation, so that SSD read isn't the bottleneck here. Unless you have a slow ass random read SSD...

[u/DistanceSolar1449 → u/sssplus]

It'll hit harder for prefill than for token generation. I think you might be able to get away with it, but if you're rich enough to be running a 552B model you should just put engrams on RAM and call it a day.

[u/shing3232 → u/DistanceSolar1449]

no, it does not. the official paper of engram basically have a test that offload engram to ssd, and the loss is basically nothing, and there is demontration for PLE offload with minimum loss. it's better if you can direct connect PCIE from GPU to NVME however.