--- title: "GLM-5.3-Flash-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4ba33faa-b477-4cc6-922a-3291f2b179c2" updated: "2026-11-12" --- # GLM-5.3-Flash-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs Unsloth's GGUF copy of the GLM-5.3-Flash text model on your own hardware, a large open model its authors aim at coding and agent work. For people with plenty of memory. GLM-5.3-Flash-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 GLM-5.3-Flash-GGUF is Unsloth's GGUF packaging of GLM-5.3-Flash, a text generation model from the zai-org team. The model has about 320B parameters in total and 18B active at a time, and the listing shows English and Chinese. The authors say a mix of sparse and linear attention keeps long-context serving cheaper. ### Who it suits It suits people who want a huge open model on hardware they control and have the memory to hold it. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | Developers | Run a large coding model locally | Asking it to review a function | | AI infrastructure | Engineers | Test how a 320B model loads and serves on their own machines | Trying the 4-bit files before a deployment | | Research | Researchers | Compare an open model with hosted ones on their own tasks | Running the same prompts through two models | | Bilingual work | Writers and translators | Draft and edit text in English and Chinese | Rewriting a paragraph in Chinese | You can skip it if your computer cannot hold roughly 160 GB of 4-bit weights, because a smaller model will fit better. ### Where to use it - The Unsloth Desktop app, which the card says can run it - A llama.cpp build that includes the pull request the card names - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Coding help | Developers | Text answers and code from a model that runs locally | | Agent experiments | Engineers | A local model to wire into tool-using workflows | | Long-document work | Analysts | Answers over long inputs that the long-context design targets | ### How to set it up You need Python for the download tool and either the desktop app or a suitable llama.cpp build. - Python on Windows, macOS or Linux - The huggingface\_hub package - Unsloth Desktop, or llama.cpp with the pull request the card points to - About 160 GB of space for the 4-bit weights, as an estimate 1. Install huggingface\_hub. 2. Download the GGUF files to your computer. 3. Open them in Unsloth Desktop, or build the llama.cpp version the card names and load them there. ```sh pip install -U huggingface_hub hf download unsloth/GLM-5.3-Flash-GGUF ``` ### How to use it The card gives no command beyond the download. You load the files in Unsloth Desktop or the llama.cpp build it names and chat with the model there. The card also shows a 1-bit Low version running in the desktop app. ```sh hf download unsloth/GLM-5.3-Flash-GGUF ``` ### Example You load the model in Unsloth Desktop and ask it to "review this Python function and suggest a fix", pasting the code. It answers with written feedback and a revised version of the function as plain text. ### For newcomers and experts If you are new to local models, this one is huge, so check your free disk space and memory before downloading. A first small try is to read the Unsloth guide the card points to, then load the files in the desktop app. For people who know the field, it is a mixture-of-experts model with 18B active of 320B total, trained from a new base with sparse and linear attention and Manifold-Constrained Hyper-Connections. The benchmark claims come from the authors, so test it on your own prompts. Parameters in billions | Label | Value | | --- | --- | | Total | 320 billion | | Active at a time | 18 billion | GGUF files download from Hugging Face → Unsloth Desktop or llama.cpp loads them → You send a text prompt → The model answers with generated text ### Good to know Stock llama.cpp may not load it yet, since the card tells you to use a specific pull request or the Unsloth Desktop app. - License: MIT - Cost to run: none named for the local files, and the card also mentions a hosted Z.ai API Platform - Data it reaches: the prompts you type and the downloaded model files - Not for: machines with limited memory, since the 4-bit weights alone are about 160 GB ## README ````markdown # GLM-5.3-Flash-GGUF Runs Unsloth's GGUF copy of the GLM-5.3-Flash text model on your own hardware, a large open model its authors aim at coding and agent work. For people with plenty of memory. 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: GLM-5.3-Flash-GGUF is Unsloth's GGUF packaging of GLM-5.3-Flash, a text generation model from the zai-org team. The model has about 320B parameters in total and 18B active at a time, and the listing shows English and Chinese. The authors say a mix of sparse and linear attention keeps long-context serving cheaper. Who it suits: It suits people who want a huge open model on hardware they control and have the memory to hold it. ```table Industry | Who uses it | How they use it | Example Software development | Developers | Run a large coding model locally | Asking it to review a function AI infrastructure | Engineers | Test how a 320B model loads and serves on their own machines | Trying the 4-bit files before a deployment Research | Researchers | Compare an open model with hosted ones on their own tasks | Running the same prompts through two models Bilingual work | Writers and translators | Draft and edit text in English and Chinese | Rewriting a paragraph in Chinese ``` You can skip it if your computer cannot hold roughly 160 GB of 4-bit weights, because a smaller model will fit better. Where to use it: - The Unsloth Desktop app, which the card says can run it - A llama.cpp build that includes the pull request the card names - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Coding help | Developers | Text answers and code from a model that runs locally Agent experiments | Engineers | A local model to wire into tool-using workflows Long-document work | Analysts | Answers over long inputs that the long-context design targets ``` How to set it up: You need Python for the download tool and either the desktop app or a suitable llama.cpp build. - Python on Windows, macOS or Linux - The huggingface_hub package - Unsloth Desktop, or llama.cpp with the pull request the card points to - About 160 GB of space for the 4-bit weights, as an estimate 1. Install huggingface_hub. 2. Download the GGUF files to your computer. 3. Open them in Unsloth Desktop, or build the llama.cpp version the card names and load them there. ```sh pip install -U huggingface_hub hf download unsloth/GLM-5.3-Flash-GGUF ``` How to use it: The card gives no command beyond the download. You load the files in Unsloth Desktop or the llama.cpp build it names and chat with the model there. The card also shows a 1-bit Low version running in the desktop app. ```sh hf download unsloth/GLM-5.3-Flash-GGUF ``` Example: You load the model in Unsloth Desktop and ask it to "review this Python function and suggest a fix", pasting the code. It answers with written feedback and a revised version of the function as plain text. For newcomers and experts: If you are new to local models, this one is huge, so check your free disk space and memory before downloading. A first small try is to read the Unsloth guide the card points to, then load the files in the desktop app. For people who know the field, it is a mixture-of-experts model with 18B active of 320B total, trained from a new base with sparse and linear attention and Manifold-Constrained Hyper-Connections. The benchmark claims come from the authors, so test it on your own prompts. ```chart Title: Parameters in billions Total: 320 billion Active at a time: 18 billion ``` ```flow GGUF files download from Hugging Face Unsloth Desktop or llama.cpp loads them You send a text prompt The model answers with generated text ``` Good to know: Stock llama.cpp may not load it yet, since the card tells you to use a specific pull request or the Unsloth Desktop app. - License: MIT - Cost to run: none named for the local files, and the card also mentions a hosted Z.ai API Platform - Data it reaches: the prompts you type and the downloaded model files - Not for: machines with limited memory, since the 4-bit weights alone are about 160 GB ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 321B - Weights: about 160 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 800,355 - Likes: 526 - License: mit - Languages: en, zh - Based on: zai-org/GLM-5.3-Flash - Quantized versions: 4 - Tags: gguf, unsloth, glm5_next, text-generation, conversational ## Links - Model: https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF - Files: https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/GLM-5.3-Flash-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/GLM-5.3-Flash-GGUF on Hugging Face](https://huggingface.co/unsloth/GLM-5.3-Flash-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 | | --- | --- | --- | --- | --- | --- | | GLM-5.3-Flash-GGUF (this page) | 800,355 | 526 | 321B | mit | 2026-10-05 | | [GLM-5.3-Flash EXL3 Spark](https://mindket.com/p/ec88db01-e5e6-420e-9171-26462734e98f) | 399 | 11 | 48B | mit | 2026-09-18 | | [Qwopus3.8-27B-Flash-V2-GGUF](https://mindket.com/p/f4542d06-f1ba-46a9-8148-58a34e8911b6) | 897,675 | 75 | 461M | apache-2.0 | 2026-09-21 | | [Ornith-1.5-122B-GGUF by unsloth](https://mindket.com/p/fc5ca61a-5ce6-4076-9ef7-f878a048728b) | 680,483 | 101 | 125B | mit | 2026-10-21 | | [gpt-oss-20b-GGUF](https://mindket.com/p/99b8d5c9-4750-4943-807b-082810629e1f) | 614,134 | 909 | 20.9B | apache-2.0 | 2025-12-19 | | [flint-GGUF](https://mindket.com/p/a49e571d-d5ee-4184-9d1b-0d7c53ad9c0b) | 325,104 | 376 | 39.8B | apache-2.0 | 2026-10-21 | 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 · walrus01 · 2026-08-30](https://news.ycombinator.com/item?id=49494534): GLM-5.3-Flash-GGUF - [Bluesky · Unsloth AI · 2026-09-04](https://bsky.app/profile/unsloth.ai/post/3muoy6iukzc2t): We made GLM-5.3-Flash run 3.3x faster locally\! Local GGUF inference is now 1.6–3.4× faster with optimized decoding and bonus multi-token prediction. Run 3-bit on 128GB setups via Unsloth Desktop or llama.cpp. Guide: unsloth.ai/docs/models/... GGUF: huggingface.co/unsloth/GLM-... - [Bluesky · Unsloth AI · 2026-08-27](https://bsky.app/profile/unsloth.ai/post/3mu33pabujs2p): GLM-5.3-Flash can now be run locally\! ✨ Run 3-bit on 128GB RAM via Unsloth GGUF. GLM-5.3-Flash (ox-alpha) rivals Claude Opus 4.8 on DeepSWE, coding \& agentic benchmarks. Guide: unsloth.ai/docs/models/... GGUF: huggingface.co/unsloth/GLM-... - [Bluesky · emily-3.5-0.4B:IQ1\_XXXS · 2026-10-12](https://bsky.app/profile/emily.news/post/3mxni2vac2k2l): huggingface.co/unsloth/GLM-... quantized at Q2\_K\_XL. bear in mind I have 128 GB of unified RAM - [Bluesky · Thomas Wood · 2026-10-03](https://bsky.app/profile/advanced-eschatonics.com/post/3mwx53znfg22s): I can absolutely fit the IQ2\_XXS into memory on my 128 GB Strix Halo - [Bluesky · デイリーHuggingFaceトレンド · 2026-09-01](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3muhn4zitsj2p): 今日のHuggingFaceトレンド unsloth/GLM-5.3-Flash-GGUF GLM-5シリーズ初のネイティブマルチモーダルモデルであるGLM-5.3-Flashを提供することを目的としています。 効率的なアーキテクチャの採用により、計算コストを抑えつつ高い性能を実現しており、特にコーディングやエージェント的なタスクにおいて高い能力を発揮します。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-08-30](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mucmqs32dg23): 今日のHuggingFaceトレンド unsloth/GLM-5.3-Flash-GGUF GLM-5シリーズ初のネイティブマルチモーダルモデルであるGLM-5.3-Flashの提供を目的としています。 ハイブリッドアーキテクチャの採用により、低コストで効率的な動作と高い知能を実現しており、特にコーディングやエージェントタスクにおいて高い性能を発揮します。 ## 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: 2026-10-05 - First published: 2026-08-26 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/GLM-5.3-Flash-GGUF on Hugging Face](https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4ba33faa-b477-4cc6-922a-3291f2b179c2) - [Its page on Mindket](https://mindket.com/p/4ba33faa-b477-4cc6-922a-3291f2b179c2)