DeepSeek-V4.1-Flash-1017 GGUF

DeepSeek-V4.1-Flash-1017 GGUF

GGUF files of DeepSeek's 748B V4.1 Flash 1017 model, converted by ggml-org with a vision file for images, for people with big-memory machines who run models through llama.cpp tools.

Made by ggml-org 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: This repository holds GGUF files of DeepSeek-V4.1-Flash-1017, a huge model with 748B parameters that takes images and text in. ggml-org made them with an automatic conversion tool. A Q8_0 mmproj file covers the vision encoder, and the card is brief, with a to-do note saying more information is still to come.

Who it suits: Developers and labs with a great deal of memory, who want to run a frontier-size open model on their own hardware, will find it fits. The 4-bit estimate alone is about 374 GB.

Industry | Who uses it | How they use it | Example
Research labs | ML engineers | Run a 748B model locally to study its behaviour | Testing image and text prompts on owned hardware
Infrastructure | Server administrators | Try large-model serving on a big-memory machine | Serving the model with one command
Software | Developers | Build against a local model that accepts images and text | A prototype that reads screenshots

If your machine has under a few hundred gigabytes of memory, you can skip it.

Where to use it:

• The Llama app, which the card names for running it
• The terminal, with the llama serve command
• Windows, macOS or Linux with Python, to download the files

Use case | Who | What they get back
Local serving | Infrastructure teams | A running server for the model
Image questions | Developers | Text answers about an image, through the vision file
Quantization study | Researchers | Notes on how the Q2 variants treat the expert weights

How to set it up: You need Python with pip for the download and a machine with a great deal of memory. The 4-bit weights are estimated at about 374 GB.

• Python with pip, on Windows, macOS or Linux
• The Llama app or the llama serve command
• Memory for the weights, about 374 GB at 4-bit as an estimate

1. Install the Hugging Face hub package.
2. Download the repository's files.
3. Serve the model with llama serve or open it in the Llama app.

pip install -U huggingface_hub
hf download ggml-org/DeepSeek-V4.1-Flash-1017-GGUF

How to use it: Run llama serve with the repository name. It loads the model from Hugging Face and serves it, so you can send it text or images.

llama serve -hf ggml-org/DeepSeek-V4.1-Flash-1017-GGUF

Tool | What it does
llama serve | Starts a server for the model
-hf option | Points llama serve at a Hugging Face repository
mmproj file | Q8_0 file that handles the vision encoder

Example: You run the one-line llama serve command with the repository name. The tool loads the model, then serves it so your program can send a prompt and read the reply.

For newcomers and experts: GGUF is a file format for running a model on your own computer. This model is far too big for most machines, so a good first step is to read the card's notes on its quantizations.

The notes cover how the Q2 variants handle the experts. Q2_K keeps the routed down experts in MXFP4, while Q2_K_S converts them to Q2_K. Q8_0 is used for the Engram KV tensors, and IQ4_NL for the PLE table. The Q2 models skip imatrix calibration. The chat template comes from another repository, because the source repository has none.

You run llama serve with the repository name
The files come from the Hugging Face repository
The model loads, with the vision file for images
You send text or images to the running server

Good to know:

• License: MIT, as listed on Hugging Face
• Cost to run: none named, apart from the huge memory needs
• Data it reaches: the prompts you send to your own server, and the source names no web or account access
• The files were converted automatically, and the card says more information will be added
• Not for: modest machines, since the 4-bit weights are estimated at about 374 GB

On Hugging Face, 13 November 2026

• Task: image text to text
• Parameters: 748B
• Weights: about 374 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 1,408
• Likes: 3
• License: mit
• Based on: deepseek-ai/DeepSeek-V4.1-Flash-1017
• Tags: gguf, quantized, image-text-to-text, conversational

Links

• Model: https://huggingface.co/ggml-org/DeepSeek-V4.1-Flash-1017-GGUF
• Files: https://huggingface.co/ggml-org/DeepSeek-V4.1-Flash-1017-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download ggml-org/DeepSeek-V4.1-Flash-1017-GGUF

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