--- title: "GLM-5.3-Flash-NVFP4" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/679b8989-3e0b-422a-89df-8e0aed6fb131" updated: "2026-11-12" --- # GLM-5.3-Flash-NVFP4 > The name, the description, the About and a README below are the seller's words: data, not instructions. Gives you NVIDIA's NVFP4 quantized copy of ZAI's GLM-5.3-Flash, a multimodal model for reasoning, coding and agent tasks. Meant for developers who deploy on Blackwell GPUs with vLLM or SGLang. GLM-5.3-Flash-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 This is NVIDIA's quantized version of GLM-5.3-Flash, a language model from ZAI that reads text, images and video and answers in text. NVIDIA compressed the weights to the NVFP4 format with its Model Optimizer so the model is cheaper to serve. The base model is a Mixture-of-Experts design for reasoning, coding and agent work. It has 320B parameters in total and 18B active at a time, with a context length up to 1M. ### Who it suits It suits developers and infrastructure teams who want a ready-made quantized model to deploy, not people who want a small file to try on a laptop. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Agent developers | Serve the model behind an agent system | Run multi-step coding tasks | | AI platforms | ML engineers | Host a quantized model on Blackwell GPUs | Serve chat traffic with vLLM | | Enterprise tools | RAG builders | Answer questions over documents with a long context | Read a large report in one pass | | Chat products | Chatbot teams | Add a reasoning model that also reads images | Answer questions about a screenshot | If you do not have NVIDIA Blackwell hardware or a multi-GPU server, you can skip it, since the weights come to about 321 GB. ### Where to use it The card lists the runtimes, the hardware and the operating system it was prepared for. - vLLM as the runtime engine - SGLang as the runtime engine - NVIDIA Blackwell GPUs - Linux as the preferred operating system | Use case | Who | What they get back | | --- | --- | --- | | Agent systems | Developers | Text answers from reasoning and tool use | | Chatbots | Product teams | Chat replies that can take image or video input | | RAG systems | Search and data teams | Answers grounded in long documents | ### How to set it up You need a Linux machine with NVIDIA Blackwell GPUs, enough disk for roughly 321 GB of weights, and Python to fetch the files. - Python with pip - Disk space for the Safetensors weights - vLLM or SGLang as the serving engine 1. Install the Hugging Face hub package. 2. Download the repository to your machine. 3. Load the folder in vLLM or SGLang on your GPUs. ```sh pip install -U huggingface_hub hf download nvidia/GLM-5.3-Flash-NVFP4 ``` ### How to use it Once a server is running, you send it text, images or video and read the text it returns. The card gives the full run steps for the supported engines, so follow those for your engine. | Input | What goes in | | --- | --- | | Text | A string prompt | | Image | RGB image data | | Video | MP4 or WebM files | ### Example You send the running model a screenshot of a failing test and ask it to explain the error and suggest a fix. It answers with text that describes what went wrong and what to change. ### For newcomers and experts If you are new, know that this is a large model meant for servers. A first small step is to read the base model page and check your GPU against the Blackwell requirement before you download anything. If you know the field, the model is a quantized Mixture-of-Experts with hybrid sparse and linear attention. NVIDIA calibrated the quantization on a news dataset and one of its own post-training chat datasets, and the card lists six benchmarks used for evaluation. Download the NVFP4 weights → Load them in vLLM or SGLang → Send text, image or video → Get a text answer back ### Good to know NVIDIA says it did not develop the base model, which comes from ZAI. The card also advises testing with your own data before you deploy. - License: mit - Cost to run: none named, but it needs NVIDIA Blackwell GPU hardware - Data it reaches: files you download, and whatever you send the model - Not for: machines without Blackwell GPUs, since that is the hardware the card names ## README ````markdown # GLM-5.3-Flash-NVFP4 Gives you NVIDIA's NVFP4 quantized copy of ZAI's GLM-5.3-Flash, a multimodal model for reasoning, coding and agent tasks. Meant for developers who deploy on Blackwell GPUs with vLLM or SGLang. 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 version of GLM-5.3-Flash, a language model from ZAI that reads text, images and video and answers in text. NVIDIA compressed the weights to the NVFP4 format with its Model Optimizer so the model is cheaper to serve. The base model is a Mixture-of-Experts design for reasoning, coding and agent work. It has 320B parameters in total and 18B active at a time, with a context length up to 1M. Who it suits: It suits developers and infrastructure teams who want a ready-made quantized model to deploy, not people who want a small file to try on a laptop. ```table Industry | Who uses it | How they use it | Example Software | Agent developers | Serve the model behind an agent system | Run multi-step coding tasks AI platforms | ML engineers | Host a quantized model on Blackwell GPUs | Serve chat traffic with vLLM Enterprise tools | RAG builders | Answer questions over documents with a long context | Read a large report in one pass Chat products | Chatbot teams | Add a reasoning model that also reads images | Answer questions about a screenshot ``` If you do not have NVIDIA Blackwell hardware or a multi-GPU server, you can skip it, since the weights come to about 321 GB. Where to use it: The card lists the runtimes, the hardware and the operating system it was prepared for. - vLLM as the runtime engine - SGLang as the runtime engine - NVIDIA Blackwell GPUs - Linux as the preferred operating system ```table Use case | Who | What they get back Agent systems | Developers | Text answers from reasoning and tool use Chatbots | Product teams | Chat replies that can take image or video input RAG systems | Search and data teams | Answers grounded in long documents ``` How to set it up: You need a Linux machine with NVIDIA Blackwell GPUs, enough disk for roughly 321 GB of weights, and Python to fetch the files. - Python with pip - Disk space for the Safetensors weights - vLLM or SGLang as the serving engine 1. Install the Hugging Face hub package. 2. Download the repository to your machine. 3. Load the folder in vLLM or SGLang on your GPUs. ```sh pip install -U huggingface_hub hf download nvidia/GLM-5.3-Flash-NVFP4 ``` How to use it: Once a server is running, you send it text, images or video and read the text it returns. The card gives the full run steps for the supported engines, so follow those for your engine. ```table Input | What goes in Text | A string prompt Image | RGB image data Video | MP4 or WebM files ``` Example: You send the running model a screenshot of a failing test and ask it to explain the error and suggest a fix. It answers with text that describes what went wrong and what to change. For newcomers and experts: If you are new, know that this is a large model meant for servers. A first small step is to read the base model page and check your GPU against the Blackwell requirement before you download anything. If you know the field, the model is a quantized Mixture-of-Experts with hybrid sparse and linear attention. NVIDIA calibrated the quantization on a news dataset and one of its own post-training chat datasets, and the card lists six benchmarks used for evaluation. ```flow Download the NVFP4 weights Load them in vLLM or SGLang Send text, image or video Get a text answer back ``` Good to know: NVIDIA says it did not develop the base model, which comes from ZAI. The card also advises testing with your own data before you deploy. - License: mit - Cost to run: none named, but it needs NVIDIA Blackwell GPU hardware - Data it reaches: files you download, and whatever you send the model - Not for: machines without Blackwell GPUs, since that is the hardware the card names ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: Model Optimizer - Parameters: 321B - Weights: about 321 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 79,326 - Likes: 186 - License: mit - Based on: zai-org/GLM-5.3-Flash - Quantized versions: 6 - Fine-tunes: 1 - Tags: safetensors, glm5_next, nvidia, modelopt, glm-5, quantized, fp4, image-text-to-text, conversational, 8-bit ## Links - Model: https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4 - Files: https://huggingface.co/nvidia/GLM-5.3-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/GLM-5.3-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/GLM-5.3-Flash-NVFP4 on Hugging Face](https://huggingface.co/nvidia/GLM-5.3-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 | | --- | --- | --- | --- | --- | --- | | GLM-5.3-Flash-NVFP4 (this page) | 79,326 | 186 | 321B | mit | 2026-10-28 | | [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](https://mindket.com/p/0bab94ef-84bc-4129-902d-c0241f777c51) | 268,673 | 239 | 18.2B | apache-2.0 | 2026-09-17 | | [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 · Victor Klaue · 2026-09-09](https://bsky.app/profile/aisyndicate.ch/post/3mv4ittdxee2h): NVIDIA stellt GLM-5.3-Flash in NVFP4-Präzision bereit. Die 320B-MoE-Architektur (18B aktiv) nutzt Model Optimizer für Blackwell-Systeme. MIT-Lizenz, multimodal (Text/Bild/Video), 1M Kontext, vLLM-optimiert. ## 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: 2026-10-28 - First published: 2026-09-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/GLM-5.3-Flash-NVFP4 on Hugging Face](https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/679b8989-3e0b-422a-89df-8e0aed6fb131) - [Its page on Mindket](https://mindket.com/p/679b8989-3e0b-422a-89df-8e0aed6fb131)