--- title: "DeepSeek-V4.1-Flash-1017-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1c185af8-98d2-4feb-a1a3-d5821c72863f" updated: "2026-11-12" --- # DeepSeek-V4.1-Flash-1017-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. DeepSeek-V4.1-Flash-1017-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by unsloth ## 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. ```sh pip install -U huggingface_hub hf download unsloth/DeepSeek-V4.1-Flash-1017-GGUF ``` 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. Terminal-Bench 4.0 score | Label | Value | | --- | --- | | 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh pip install -U huggingface_hub hf download unsloth/DeepSeek-V4.1-Flash-1017-GGUF ``` ```table 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. ```flow 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. ```chart 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: ```sh 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. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [unsloth/DeepSeek-V4.1-Flash-1017-GGUF on Hugging Face](https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | DeepSeek-V4.1-Flash-1017-GGUF (this page) | 1,295,742 | 385 | 748B | mit | 2026-11-02 | | [DeepSeek-V4.1-Flash-1017 EXL3 EAQ v1 2.9 bpw](https://mindket.com/p/e2aa9049-7d40-46f6-b187-57f9ffaaccf1) | 8,113 | 46 | 105B | mit | 2026-10-20 | | [DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata](https://mindket.com/p/6a2502f6-4851-488e-b942-b3e466f51991) | 22,196 | 49 | 14.7B | mit | 2026-10-19 | | [DeepSeek-V4.1-Flash-1017 EXL3 2.9 bpw](https://mindket.com/p/037efcc1-e5e0-465b-9448-56fd5a06fdc6) | 7,382 | 60 | 105B | mit | 2026-10-19 | | [Qwen3.6-35B-A3B-MTP-GGUF](https://mindket.com/p/90db7fe0-7044-4238-a508-5d2ecb760999) | 2,315,167 | 1,051 | 35.5B | apache-2.0 | 2026-05-20 | | [Qwen3.6-35B-A3B-GGUF](https://mindket.com/p/9ac02ca9-6ba3-4ea0-b3f6-dcaea6e7789f) | 1,649,871 | 1,789 | 34.7B | apache-2.0 | 2026-04-20 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · ericpruitt · 2026-11-07](https://news.ycombinator.com/item?id=50409154): MaxxBench – "Nice DeepSWE score. But is it true?": I'm curious what quantization and version you're using. I found DeepSeek v4 to be practically useless when I was using frob/deepseek-v4-flash-0731 via Ollama, but later I switched to using unsloth/DeepSeek-V4.1-Flash-1017-GGUF, and I've been extremely happy with using it for coding tasks since then. - [Bluesky · Unsloth AI · 2026-10-19](https://bsky.app/profile/unsloth.ai/post/3myais5flhc2t): DeepSeek V4.1-Flash-1017 can now run locally\! 🐳 We made 1-bit quants retain \~88.2% accuracy while shrinking size from 508GB to 250GB (−52%). Run on a 128GB+ RAM device. V4.1 Flash 1017 is the strongest open model to date. Guide: unsloth.ai/docs/models/... GGUF: huggingface.co/unsloth/Deep... - [Bluesky · Unsloth AI · 2026-10-30](https://bsky.app/profile/unsloth.ai/post/3mz3ue2m2gs2t): We’re releasing new DeepSeek-V4.1-Flash-1017 GGUFs, ranking highest across benchmarks. Dynamic V3.1 leads across nearly all benchmarks: GPQA Diamond, AIME25, LiveCodeBench v6, MMLU-Pro, Aider \& more. Run on 128GB or 64GB+ RAM. GGUF: huggingface.co/unsloth/Deep... Blog: unsloth.ai/docs/models/... - [Bluesky · Unsloth AI · 2026-11-02](https://bsky.app/profile/unsloth.ai/post/3mzdkwyf4yk2t): We benchmarked DeepSeek-V4.1-Flash-1017 GGUFs on Terminal Bench, DeepSWE \& more on 512K context. Unsloth ranks highest overall. 1-bit Unsloth GGUF scores 68.1 on DeepSWE, 72.2 on Terminal-Bench, 52.7 on AutomationBench. \>10% ahead of other quants. GGUF: huggingface.co/unsloth/Deep... - [Bluesky · UndergroundAI · 2026-11-03](https://bsky.app/profile/undergroundai66.bsky.social/post/3mzgq6fkfdh24): ⚡ Das neueste multimodale Trend-Modell ist da. 🛠️ Entdecke das von Unsloth bereitgestellte Modell DeepSeek-V4.1-Flash-1017-GGUF für Bild und Text auf… 🚀 Erfahre mehr dazu direkt bei Underground AI. 👉 https://underground-ai.bolt.app/ \#OpenSource \#KuenstlicheIntelligenz ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-02 - First published: 2026-10-17 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/DeepSeek-V4.1-Flash-1017-GGUF on Hugging Face](https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1c185af8-98d2-4feb-a1a3-d5821c72863f) - [Its page on Mindket](https://mindket.com/p/1c185af8-98d2-4feb-a1a3-d5821c72863f)