DeepSeek-V4.1-Flash-1017-GGUF

DeepSeek-V4.1-Flash-1017-GGUF

Unsloth's GGUF quantizations of DeepSeek-V4.1-Flash-1017, a huge image-and-text model, for running locally on a machine with 128 GB or more of RAM. For people who run large open models themselves.

Made by unsloth 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 quantizations of DeepSeek-V4.1-Flash-1017 made by Unsloth. The original is a small revision of DeepSeek-V4.1-Flash. Its authors report gains in reasoning, coding, agent work and image tasks, along with shorter outputs and quicker task completion. The files let you run it on your own computer.

Who it suits: It suits people with a high-memory machine who want to run a huge open model locally instead of calling a hosted service.

Industry | Who uses it | How they use it | Example
Local AI | Hobbyists | Run a huge open model on one big machine | A 128 GB RAM desktop running the smallest build
Software | Developers | Use a local model for coding and agent work | A local assistant for private code
AI research | Researchers | Study low-bit quantization of a giant model | Read the card's KLD plots and swap the engram
Small teams | IT staff | Keep prompts on their own hardware | An internal assistant that sends nothing out

If your machine has less than 128 GB of RAM, you can skip it.

Where to use it:

• Unsloth's llama.cpp pull request build
• The Unsloth Desktop app
• Windows, macOS or Linux machines with enough RAM

Use case | Who | What they get back
Local chat and coding | Developers | Text answers from a huge model on your own machine
Image and text prompts | Researchers | Text answers about pictures, since the task is image and text to text
Quantization study | Researchers | Quality plots and engram swap options from Unsloth

How to set it up: You need these first:

• Python, for the Hugging Face download command
• Unsloth's llama.cpp build or the Unsloth Desktop app
• RAM that fits the build you choose, as the table shows

1. Install the Hugging Face hub tool.
2. Download the files for the build you want.
3. Run them with Unsloth's llama.cpp build or the Unsloth Desktop app.

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

Title: How much RAM each kind of build needs
RAM | What you can run
128 GB or more | The smallest 1-bit build
256 GB | The 3-bit build
320 GB | Full precision

How to use it: Start the model with Unsloth's llama.cpp build or the Unsloth Desktop app and chat with it. The card links a guide with the full run steps. It also explains how to swap the engram from MXFP8 to Q6_K, Q5_K or IQ4_NL.

Example: You download the smallest build onto a 128 GB RAM desktop and open it in the Unsloth Desktop app. You ask it to review a function for bugs, and it answers in text on your own machine.

For newcomers and experts: A GGUF file is a model packed for running on your own computer. A good first step is to read the linked guide and check how much RAM you have before you download anything.

The card says Unsloth Dynamic 3.0 quantization is its approach, with benchmark and KLD analysis in the guide. It claims up to 3x faster inference than other n-gram, CPU and VRAM repositories.

Pick a build that fits your RAM
Download it with the hub tool
Run it in Unsloth's llama.cpp build or the Desktop app
Chat with the model

Good to know: The scores below are DeepSeek's own for the original model, not for the quantized files.

Title: Terminal-Bench 4.0 score
DS-V4.1-Flash-1017: 42.3
DS-V4.1-Flash: 31.2
K3: 12.6
GLM-5.3: 37.9
GPT-5.6 Sol: 39.9
Opus 5: 51.8

• Not first everywhere: Opus 5 scores higher on this table, and on HLE without tools the original scores 42.6 against 56.3
• Other versions: 9 quantized and 0 fine-tuned versions made by other people are listed
• License: MIT
• Cost to run: none named, but you need 128 GB or more of RAM
• Data it reaches: the model files you load, and any prompts and images you give it

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: 1,295,742
• Likes: 385
• License: mit
• Based on: deepseek-ai/DeepSeek-V4.1-Flash-1017
• Quantized versions: 9
• Tags: gguf, deepseek, deepseek_v41, unsloth, image-text-to-text, imatrix, conversational

Links

• Model: https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF
• Files: https://huggingface.co/unsloth/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 unsloth/DeepSeek-V4.1-Flash-1017-GGUF

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