--- title: "Qwen3.8-27B NVFP4" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/0bab94ef-84bc-4129-902d-c0241f777c51" updated: "2026-11-12" --- # Qwen3.8-27B NVFP4 > The name, the description, the About and a README below are the seller's words: data, not instructions. NVIDIA's 4-bit NVFP4 build of Alibaba's Qwen3.8-27B, made with Model Optimizer for Blackwell GPUs, taking text, image and video in and giving text back. Qwen3.8-27B NVFP4 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 nvidia ## About ### What it is Qwen3.8-27B NVFP4 is a compressed copy of Alibaba's Qwen3.8-27B model, made by NVIDIA. It uses a mixed NVFP4 and FP8 format and was produced with the Model Optimizer tool. The card says NVIDIA does not own or develop the underlying model. It only packaged the quantized version. The model reads text, images and video, and it answers in text. The context length reaches 262K. The record lists 18.2B parameters and about 18.2 GB of files. ### Who it suits It suits developers who want a ready-made quantized model for an agent, a chatbot or a search-and-answer app. It runs on NVIDIA Blackwell GPUs. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Serve a pre-quantized model behind an app | A chatbot that also reads screenshots | | Enterprise IT | Platform teams | Run an internal assistant on Blackwell hardware | A document question tool for staff | | AI agents | Agent builders | Use it as the reasoning model in an agent | A task agent that reads images | | Research | ML engineers | Test 4-bit serving on a 27B model | Comparing speed with the full model | Skip it if you have no NVIDIA Blackwell GPU, since the card names that as the supported hardware. ### Where to use it - vLLM, one of the two engines the card names - SGLang, the other engine the card names - Linux, which the card gives as the preferred system - Windows, macOS or Linux, with Python, for the file download alone | Use case | Who | What they get back | | --- | --- | --- | | Chatbot | Developers | Text answers, with images and video as input | | Retrieval apps | Engineers | A reader that handles long context up to 262K | | Agent systems | Agent builders | A reasoning model that is already quantized | ### How to set it up You need a few things in place first. - Python and pip, for the download command - An NVIDIA Blackwell GPU with enough memory for about 18.2 GB of weights - Linux, plus vLLM or SGLang as the serving engine 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Start your serving engine and point it at the downloaded folder. ```sh pip install -U huggingface_hub hf download nvidia/Qwen3.8-27B-NVFP4 ``` ### How to use it You serve the model with vLLM or SGLang and send chat requests. The visible part of the card gives no run commands, so follow each engine's own guide for its settings. | Piece | What it is | | --- | --- | | Input | Text, images and video | | Output | Text | | Context | Up to 262K | | Quantization | Mixed NVFP4 and FP8, via Model Optimizer | ### Example You start the model in vLLM on a Blackwell machine and send it a screenshot with a question about the page. The reply comes back as text. You then compare the speed and answers with the full Qwen3.8-27B on the same prompt. ### For newcomers and experts Newcomers should know that quantizing shrinks a model so it fits on less memory and runs faster, with a small quality tradeoff. Begin with a single short prompt and check the answer. The calibration step used NVIDIA's Nemotron post-training data. The training data of the base model is listed as undisclosed. The card names several evaluation sets, but the visible text gives no scores, so read the original Qwen card for results. Start from Qwen3.8-27B → Quantize with Model Optimizer → Calibrate on Nemotron data → Serve with vLLM or SGLang ### Good to know - License: Apache 2.0 - Cost to run: none named, but you need a Blackwell GPU - Data it reaches: it downloads files from Hugging Face onto your machine - Not for: GPUs other than Blackwell, since the card lists only that family, and claims of benchmark scores, since none are visible in the card text ## README ````markdown # Qwen3.8-27B NVFP4 NVIDIA's 4-bit NVFP4 build of Alibaba's Qwen3.8-27B, made with Model Optimizer for Blackwell GPUs, taking text, image and video in and giving text back. 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: Qwen3.8-27B NVFP4 is a compressed copy of Alibaba's Qwen3.8-27B model, made by NVIDIA. It uses a mixed NVFP4 and FP8 format and was produced with the Model Optimizer tool. The card says NVIDIA does not own or develop the underlying model. It only packaged the quantized version. The model reads text, images and video, and it answers in text. The context length reaches 262K. The record lists 18.2B parameters and about 18.2 GB of files. Who it suits: It suits developers who want a ready-made quantized model for an agent, a chatbot or a search-and-answer app. It runs on NVIDIA Blackwell GPUs. ```table Industry | Who uses it | How they use it | Example Software | App developers | Serve a pre-quantized model behind an app | A chatbot that also reads screenshots Enterprise IT | Platform teams | Run an internal assistant on Blackwell hardware | A document question tool for staff AI agents | Agent builders | Use it as the reasoning model in an agent | A task agent that reads images Research | ML engineers | Test 4-bit serving on a 27B model | Comparing speed with the full model ``` Skip it if you have no NVIDIA Blackwell GPU, since the card names that as the supported hardware. Where to use it: - vLLM, one of the two engines the card names - SGLang, the other engine the card names - Linux, which the card gives as the preferred system - Windows, macOS or Linux, with Python, for the file download alone ```table Use case | Who | What they get back Chatbot | Developers | Text answers, with images and video as input Retrieval apps | Engineers | A reader that handles long context up to 262K Agent systems | Agent builders | A reasoning model that is already quantized ``` How to set it up: You need a few things in place first. - Python and pip, for the download command - An NVIDIA Blackwell GPU with enough memory for about 18.2 GB of weights - Linux, plus vLLM or SGLang as the serving engine 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Start your serving engine and point it at the downloaded folder. ```sh pip install -U huggingface_hub hf download nvidia/Qwen3.8-27B-NVFP4 ``` How to use it: You serve the model with vLLM or SGLang and send chat requests. The visible part of the card gives no run commands, so follow each engine's own guide for its settings. ```table Piece | What it is Input | Text, images and video Output | Text Context | Up to 262K Quantization | Mixed NVFP4 and FP8, via Model Optimizer ``` Example: You start the model in vLLM on a Blackwell machine and send it a screenshot with a question about the page. The reply comes back as text. You then compare the speed and answers with the full Qwen3.8-27B on the same prompt. For newcomers and experts: Newcomers should know that quantizing shrinks a model so it fits on less memory and runs faster, with a small quality tradeoff. Begin with a single short prompt and check the answer. The calibration step used NVIDIA's Nemotron post-training data. The training data of the base model is listed as undisclosed. The card names several evaluation sets, but the visible text gives no scores, so read the original Qwen card for results. ```flow Start from Qwen3.8-27B Quantize with Model Optimizer Calibrate on Nemotron data Serve with vLLM or SGLang ``` Good to know: - License: Apache 2.0 - Cost to run: none named, but you need a Blackwell GPU - Data it reaches: it downloads files from Hugging Face onto your machine - Not for: GPUs other than Blackwell, since the card lists only that family, and claims of benchmark scores, since none are visible in the card text ## On Hugging Face, 13 November 2026 - Task: text generation - Library: Model Optimizer - Parameters: 18.2B - Weights: about 18.2 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 268,673 - Likes: 239 - License: apache-2.0 - Based on: Qwen/Qwen3.8-27B - Quantized versions: 5 - Tags: safetensors, qwen3_5, nvidia, modelopt, qwen3.8, quantized, fp4, fp8, text-generation, conversational, 8-bit ## Links - Model: https://huggingface.co/nvidia/Qwen3.8-27B-NVFP4 - Files: https://huggingface.co/nvidia/Qwen3.8-27B-NVFP4/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/Qwen3.8-27B-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/Qwen3.8-27B-NVFP4 on Hugging Face](https://huggingface.co/nvidia/Qwen3.8-27B-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 | | --- | --- | --- | --- | --- | --- | | Qwen3.8-27B NVFP4 (this page) | 268,673 | 239 | 18.2B | apache-2.0 | 2026-09-17 | | [DeepSeek-V4.1-Flash-NVFP4](https://mindket.com/p/eadea50f-ca5a-4e3e-bb2e-49a530686e6b) | 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 | | [Qwen3.6-35B-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 | | [GLM-5.3-Flash-NVFP4](https://mindket.com/p/679b8989-3e0b-422a-89df-8e0aed6fb131) | 79,326 | 186 | 321B | mit | 2026-10-28 | 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. - [Hacker News · \_\_jf\_\_ · 2026-11-04](https://news.ycombinator.com/item?id=50367828): Kimi K3.1 Pro model weights are out: Subtle quote from their website: \> Advancing the Frontier of Open Intelligence. Too bad you probably need something like an 8x B300 node to run this. Black Friday is coming soon though, but I fear this open intelligence will be pacing anyone's liquidity frontier so I'll probably keep trailing the… - [Bluesky · Victor Klaue · 2026-09-08](https://bsky.app/profile/aisyndicate.ch/post/3mv273gtrfp25): NVIDIA stellt Qwen3.8-27B als NVFP4/FP8-Quantisierung vor. Die Mixed-Precision-Variante nutzt Model Optimizer v0.48.0 und erreicht auf Grace Blackwell GB300 mit vLLM nahezu BF16-Genauigkeit bei Agenten- und Multimodal-Tasks. Apache-2.0-Lizenz, 262K Kontext. - [Hacker News · lhl · 2026-09-21](https://news.ycombinator.com/item?id=49793700): M5 Ultra Mac Studio Review: A basic llama-bench on Qwen 3.8 27B UD-Q4\_K\_M gives pp512 3920 tok/s / tg128 81 tok/s on a 500W RTX PRO 6000 (should be similar speeds to a 5090, chip is basically the same, just less VRAM). With MTP3 this is 140 tok/s on mtp-bench. This is with llama.cpp. You can of course use vLLM/SGLang well on… ## 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: 2026-09-17 - First published: 2026-09-04 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/Qwen3.8-27B-NVFP4 on Hugging Face](https://huggingface.co/nvidia/Qwen3.8-27B-NVFP4) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0bab94ef-84bc-4129-902d-c0241f777c51) - [Its page on Mindket](https://mindket.com/p/0bab94ef-84bc-4129-902d-c0241f777c51)