--- title: "DeepSeek-V4.1-Flash-NVFP4" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/eadea50f-ca5a-4e3e-bb2e-49a530686e6b" updated: "2026-11-13" --- # DeepSeek-V4.1-Flash-NVFP4 > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads text and images and writes text, as a smaller NVFP4 copy of DeepSeek-V4.1-Flash made by NVIDIA. For developers deploying agents, chatbots and retrieval systems on NVIDIA Blackwell GPUs. DeepSeek-V4.1-Flash-NVFP4 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by nvidia ## About ### What it is This is NVIDIA's quantized copy of DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts language model that takes text and images in and writes text out. NVIDIA made it with Model Optimizer. Context can reach up to one million tokens. ### Who it suits Developers who want a pre-quantized model they can deploy as is, instead of quantizing one themselves. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | AI engineers | Serve the model behind an agent system | Run an agent that reads screenshots and text | | Support | Chatbot builders | Use it as the model inside a chatbot | Answer questions about a pasted image | | Search | Retrieval builders | Use it to write answers from retrieved documents | Answer from long documents in one prompt | Small teams without Blackwell GPUs and a lot of disk space can skip it. ### Where to use it - Linux machines with NVIDIA Blackwell GPUs - vLLM, but only a build that can load the MXFP4 Engram tables - SGLang, but only the previous revision of the files | Use case | Who | What they get back | | --- | --- | --- | | Agent systems | Developers | A model that reads text and images and writes text | | Chatbots | Developers | Replies from a large model in a smaller quantized form | | Retrieval systems | Developers | Answers written over long context | ### How to set it up - Python with pip, to fetch the files - A Linux machine with NVIDIA Blackwell hardware - About 763 GB of disk space for the weights - A vLLM build that supports the MXFP4 Engram tables, or the previous revision of the files 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Serve them with a supported vLLM build, or pin the previous revision if your build cannot load the current one. ```sh pip install -U huggingface_hub hf download nvidia/DeepSeek-V4.1-Flash-NVFP4 ``` To pin the previous revision, the card gives this flag. ```text --revision 3431dde3247c13b5957f682b1e3c6fcae2566079 ``` ### How to use it Once the model is served, you send it text, or text with an image, and read back text. The card does not list a serve command, so follow the runtime's own instructions for loading it. ### Example You send a screenshot of a chart with the question of what trend it shows. The model reads the image and the question together and writes a text answer. This is an illustration of the input and output types the card lists, not a tested run. ### For newcomers and experts If you are new, know that this is an enormous model. Downloading it needs hundreds of gigabytes, and the card names only Blackwell GPUs on Linux. A first small step is to read DeepSeek's own model card for the original. For experts, the card counts 552B parameters in the backbone. The model activates 8B while reading the prompt and 16B while writing, and adds a 196B Engram memory. Weights are stored in NVFP4, and the Engram lookup tables in MXFP4 since the October update. DeepSeek-V4.1-Flash is the base model → Model Optimizer quantizes it to NVFP4 → You download the files from Hugging Face → vLLM serves it on Blackwell GPUs ### Good to know NVIDIA did not build the original model. The card says it was made to a third party's requirements and NVIDIA only quantized it. It also says systems built on it need extra testing with your own data. - License: MIT, as the Hugging Face record lists it. - Cost to run: no API key or account is named. You need Blackwell hardware and about 763 GB of weights. - Data it reaches: downloading reaches Hugging Face. The card does not describe the model reaching files, the web or an account. - Not for: stock vLLM images cannot load the current revision, and SGLang was not validated with the MXFP4 tables. ## README ````markdown # DeepSeek-V4.1-Flash-NVFP4 Reads text and images and writes text, as a smaller NVFP4 copy of DeepSeek-V4.1-Flash made by NVIDIA. For developers deploying agents, chatbots and retrieval systems on NVIDIA Blackwell GPUs. Made by nvidia 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 is NVIDIA's quantized copy of DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts language model that takes text and images in and writes text out. NVIDIA made it with Model Optimizer. Context can reach up to one million tokens. Who it suits: Developers who want a pre-quantized model they can deploy as is, instead of quantizing one themselves. ```table Industry | Who uses it | How they use it | Example Software | AI engineers | Serve the model behind an agent system | Run an agent that reads screenshots and text Support | Chatbot builders | Use it as the model inside a chatbot | Answer questions about a pasted image Search | Retrieval builders | Use it to write answers from retrieved documents | Answer from long documents in one prompt ``` Small teams without Blackwell GPUs and a lot of disk space can skip it. Where to use it: - Linux machines with NVIDIA Blackwell GPUs - vLLM, but only a build that can load the MXFP4 Engram tables - SGLang, but only the previous revision of the files ```table Use case | Who | What they get back Agent systems | Developers | A model that reads text and images and writes text Chatbots | Developers | Replies from a large model in a smaller quantized form Retrieval systems | Developers | Answers written over long context ``` How to set it up: - Python with pip, to fetch the files - A Linux machine with NVIDIA Blackwell hardware - About 763 GB of disk space for the weights - A vLLM build that supports the MXFP4 Engram tables, or the previous revision of the files 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Serve them with a supported vLLM build, or pin the previous revision if your build cannot load the current one. ```sh pip install -U huggingface_hub hf download nvidia/DeepSeek-V4.1-Flash-NVFP4 ``` To pin the previous revision, the card gives this flag. ```text --revision 3431dde3247c13b5957f682b1e3c6fcae2566079 ``` How to use it: Once the model is served, you send it text, or text with an image, and read back text. The card does not list a serve command, so follow the runtime's own instructions for loading it. Example: You send a screenshot of a chart with the question of what trend it shows. The model reads the image and the question together and writes a text answer. This is an illustration of the input and output types the card lists, not a tested run. For newcomers and experts: If you are new, know that this is an enormous model. Downloading it needs hundreds of gigabytes, and the card names only Blackwell GPUs on Linux. A first small step is to read DeepSeek's own model card for the original. For experts, the card counts 552B parameters in the backbone. The model activates 8B while reading the prompt and 16B while writing, and adds a 196B Engram memory. Weights are stored in NVFP4, and the Engram lookup tables in MXFP4 since the October update. ```flow DeepSeek-V4.1-Flash is the base model Model Optimizer quantizes it to NVFP4 You download the files from Hugging Face vLLM serves it on Blackwell GPUs ``` Good to know: NVIDIA did not build the original model. The card says it was made to a third party's requirements and NVIDIA only quantized it. It also says systems built on it need extra testing with your own data. - License: MIT, as the Hugging Face record lists it. - Cost to run: no API key or account is named. You need Blackwell hardware and about 763 GB of weights. - Data it reaches: downloading reaches Hugging Face. The card does not describe the model reaching files, the web or an account. - Not for: stock vLLM images cannot load the current revision, and SGLang was not validated with the MXFP4 tables. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: Model Optimizer - Parameters: 763B - Weights: about 763 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 33,272 - Likes: 128 - License: mit - Based on: deepseek-ai/DeepSeek-V4.1-Flash - Quantized versions: 1 - Tags: safetensors, deepseek_v41, nvidia, modelopt, deepseekv4, quantized, fp4, image-text-to-text, 8-bit, fp8 ## Links - Model: https://huggingface.co/nvidia/DeepSeek-V4.1-Flash-NVFP4 - Files: https://huggingface.co/nvidia/DeepSeek-V4.1-Flash-NVFP4/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/DeepSeek-V4.1-Flash-NVFP4 ``` 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 [nvidia/DeepSeek-V4.1-Flash-NVFP4 on Hugging Face](https://huggingface.co/nvidia/DeepSeek-V4.1-Flash-NVFP4) ## 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-NVFP4 (this page) | 33,272 | 128 | 763B | mit | 2026-10-14 | | [DeepSeek-V4.1-Flash-1017-NVFP4](https://mindket.com/p/55f4d177-0a71-40fb-8807-e1d706971f4e) | 13,127 | 188 | 665B | mit | 2026-11-04 | | [Qwen3.8-27B NVFP4](https://mindket.com/p/0bab94ef-84bc-4129-902d-c0241f777c51) | 268,673 | 239 | 18.2B | apache-2.0 | 2026-09-17 | | [GLM-5.3-Flash-NVFP4](https://mindket.com/p/679b8989-3e0b-422a-89df-8e0aed6fb131) | 79,326 | 186 | 321B | mit | 2026-10-28 | | [Qwen3.6-35B-A3B-NVFP4](https://mindket.com/p/eb2cda8c-206e-4479-89fa-20515e3a0444) | 4,104,345 | 678 | 18.7B | apache-2.0 | 2026-08-29 | | [Qwen3.8-27B-NVFP4 by RadixArk](https://mindket.com/p/27f312aa-8861-40a5-9689-d554e8e468f0) | 979,445 | 126 | 18.2B | apache-2.0 | 2026-08-22 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Hacker \& Security News · 2026-11-05](https://bsky.app/profile/hacker.at.thenote.app/post/3mzkjuswytk2r): NVIDIA's DeepSeek-V4.1-Flash NVFP4 Model: Everything You Need to Know Discover NVIDIA's quantized DeepSeek-V4.1-Flash NVFP4 model, featuring 552B parameters, multimodal inputs, and a 1M-token context window. \#hackernews \#news \#nvidia - [Bluesky · Botty.bot · 2026-09-17](https://bsky.app/profile/infosecbot.bsky.social/post/3mvpuhgzynq2v): Official DeepSeek v4.1 Flash NVFP4 from @NVIDIAAI 🔥 I will be testing it vs native weights and my EXL3 quant. https://t.co/wmOGoOTOaG — from @MiaAI\_lab (https://x.com/MiaAI\_lab/status/2100590722512548133) - [Bluesky · Victor Klaue · 2026-09-17](https://bsky.app/profile/aisyndicate.ch/post/3mvoylokq7225): Nvidia veröffentlicht DeepSeek-V4.1-Flash als NVFP4-quantisiertes Modell. Die FP4-Optimierung senkt Speicherbedarf und Latenz für lokale Inferenz, ohne die Architektur des Basismodells zu ändern. - [Bluesky · Victor Klaue · 2026-09-17](https://bsky.app/profile/aisyndicate.ch/post/3mvojjgho7y2d): NVIDIA veröffentlicht DeepSeek-V4.1-Flash als NVFP4-Quantisierung (W4A4) für Blackwell-Hardware. Das multimodale MoE-Modell nutzt Compressed Sparse Attention 2 und unterstützt 1M-Token-Kontext. Deployment erfordert vier GB300 GPUs via SGLang oder vLLM. ## 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: 2026-10-14 - First published: 2026-09-16 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/DeepSeek-V4.1-Flash-NVFP4 on Hugging Face](https://huggingface.co/nvidia/DeepSeek-V4.1-Flash-NVFP4) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/eadea50f-ca5a-4e3e-bb2e-49a530686e6b) - [Its page on Mindket](https://mindket.com/p/eadea50f-ca5a-4e3e-bb2e-49a530686e6b)