DeepSeek-V4.1-Flash

DeepSeek-V4.1-Flash

DeepSeek's multimodal mixture of experts model with a 552B backbone and a one million token context, built to cut KV cache cost for long input agent work. Suits teams with large GPU clusters.

Made by deepseek-ai on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: DeepSeek-V4.1-Flash is a multimodal mixture of experts model from DeepSeek. It reads images and text and writes text, with a 552B parameter backbone and a context of up to one million tokens. Its encoder decoder design projects the decoder's global KV cache from the final encoder states. Per token, it wakes just 8B parameters while reading input and 16B while writing, according to the card.

Who it suits: Teams with large GPU clusters who want an open model for long input agent work, and researchers who study KV cache compression.

Industry | Who uses it | How they use it | Example
Software | Agent developers | Run agents that read long inputs | Feed in a large codebase and ask for a plan
Research | Efficiency researchers | Study KV cache compression | Read the technical report
Infrastructure | Serving teams | Test hosting a huge mixture of experts model | Plan storage for about 763 GB of weights
Document work | Analysts | Ask about images and long documents | Ask a question about a chart and a report

Skip it if you only have a single computer, because the weights take about 763 GB.

Where to use it:

• A multi GPU server with the Transformers library, which the page lists
• Agent tools that you connect to a copy you serve yourself
• Your own storage, filled with the Hugging Face tools

Use case | Who | What they get back
Long input agent work | Developers | Replies that use contexts of up to a million tokens
Image and text questions | Analysts | Text answers that use both pictures and words
Efficiency research | Researchers | A model built around cheaper KV caches

How to set it up:

• Python and the Hugging Face tools
• About 763 GB of storage for the weights
• A large GPU cluster

1. Install the Hugging Face hub package.
2. Download the repository files.
3. Serve them with your own inference setup, since the card text here lists no command.

pip install -U huggingface_hub
hf download deepseek-ai/DeepSeek-V4.1-Flash

How to use it: Send text, images or both to a served copy. The card text here shows no client command, so follow the serving notes on the model page.

Tool | What it does
Causal Encoder Decoder | Splits 40 layers into a 20 layer encoder and a 20 layer decoder
CSA2 | Gives each attention layer a Full, Reindex or Reuse mode to share KV data
FP4 main KV caching | Stores the main KV cache in a compact 4 bit format
Engram memory | A lookup table of 196B parameters read sparsely per token
DSpark | Speculative decoding that schedules its checks by how sure the draft is

Example: Paste a long report into a served copy, attach a chart image and ask for a summary of what the chart says. It should return text that uses both the report and the picture.

For newcomers and experts: If you are new to this, know that the model is far too large for a home computer. Read the model page first and start with a smaller model.

If you know the field, each mixture of experts layer has 1 shared expert and 384 routed experts, with 6 routed experts active per token. The vision side is a DeepSeek-ViT encoder with a two layer MLP projector. Pre-training used 45T tokens, with context extended to one million tokens at 34T.

Images and text go in
The 20 layer encoder reads them
The decoder uses the projected KV cache
Text is written out

Title: Parameters in billions
Backbone: 552 billion
Active at decode: 16 billion
Active at prefill: 8 billion

Good to know:

• Hugging Face counts 763B parameters, which fits the 552B backbone plus the Engram tables.
• The model reads images and text and writes only text.
• License: mit.
• Cost to run: none named, but the hardware needs are huge.
• Data it reaches: the prompts and images you give it. The source names no account or outside service.
• Not for: the source names none.

On Hugging Face, 13 November 2026

• Task: image text to text
• Library: transformers
• Parameters: 763B
• Weights: about 763 GB in I8
• Formats: Safetensors
• Downloads in the last 30 days: 1,759,335
• Likes: 4,949
• License: mit
• Quantized versions: 143
• Fine-tunes: 27
• Tags: transformers, safetensors, deepseek_v41, text-generation, image-text-to-text, conversational, 8-bit, fp8

Links

• Model: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash
• Files: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download deepseek-ai/DeepSeek-V4.1-Flash

Then follow the usage in its model card. Nothing in this kit runs by itself.