--- title: "Muse Glimmer 30B GGUF by meta-models" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2dd183ee-1c55-44aa-9c16-c26b1ee436ef" updated: "2026-11-13" --- # Muse Glimmer 30B GGUF by meta-models > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF builds of a 30-billion-parameter agentic model for llama.cpp, with an optional image encoder and a speculative decoding drafter. They suit people who run models locally on a 24 GB GPU. Muse Glimmer 30B GGUF by meta-models 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 meta-models ## About ### What it is This repository holds GGUF conversions of Muse Glimmer 30B for llama.cpp. The card calls the model a 30-billion-parameter language model with its own perception encoder, built for agent tasks on consumer hardware and meant to run locally with no cloud access. You get two quantized text builds, an image encoder file and an optional drafter file for speculative decoding. The card names Meta Superintelligence Lab as the authors. ### Who it suits It suits people who want a large agent-style model running on their own machine through llama.cpp. A 24 GB GPU handles the smaller text build. Who might use it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Hobbyists with a 24 GB GPU | Run a private assistant with no cloud calls | Serve the model on the same computer and chat with it | | Software | Agent developers | Point tool-using agents at a local server | Test multi-step tasks against a model you control | | Research | Multimodal tinkerers | Add image input with the perception encoder | Load the encoder file next to the text build | If you need full precision, you can skip it. No bf16 GGUF is published here, and the card sends you to the base repository for that. ### Where to use it It runs in llama.cpp, using llama-server, llama-cli or llama-mtmd-cli. The card needs a build numbered b10353 or newer. - llama.cpp on Windows, macOS or Linux - A CUDA GPU, a CPU only, or Apple Metal, depending on how you build - An OpenAI-style chat endpoint served by llama-server Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Local chat and reasoning | Anyone with a big GPU | Answers, with the thinking returned in a separate field | | Image questions | Multimodal users | Text answers about a picture, once the encoder file is loaded | | Faster generation | Power users | Speculative decoding with the drafter file | ### How to set it up Get a recent llama.cpp first, because older builds cannot load these files at all. Then download the weights you need and start the server. - llama.cpp release b10353 or newer - About 17 GB of memory for the smaller text build, more with extras - Python with pip, for the Hugging Face tools 1. Download a llama.cpp release tagged b10353 or later, or build it from source. 2. Check the version number is at least 10353. 3. Download the text build and the encoder file. 4. Start llama-server with the model and the encoder. ```bash ./llama-cli --version # build number must be >= 10353 ``` ```bash cd llama.cpp cmake -B build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON cmake --build build --config Release -j \ --target llama-cli llama-mtmd-cli llama-server ``` ```bash pip install huggingface_hub hf download meta-models/Muse-Glimmer-30B-GGUF \ --local-dir Muse-Glimmer-30B-GGUF \ ``` Put your own value in its key setting first. ### How to use it Run llama-server with the text build and the encoder file, then send chat requests. The answer arrives in the content field and the thinking in a separate reasoning field. To add the drafter, pass its file with the extra flags. ```bash ./build/bin/llama-server \ -m Muse-Glimmer-30B-GGUF/Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf \ --mmproj Muse-Glimmer-30B-GGUF/mmproj-Muse-Glimmer-30B-Q4_K_M.gguf \ -a muse-glimmer-30B \ -ngl 99 -c 131072 -np 4 \ --host 127.0.0.1 --port 8080 \ --jinja \ --temp 1.0 --top-p 0.95 --top-k 64 ``` ```bash -md Muse-Glimmer-30B-GGUF/dflash-Muse-Glimmer-30B-Q4_K_M.gguf -ngld 99 ``` The files | Tool | What it does | | --- | --- | | Q4\_K\_M text build | The main text model, the card's starting point for 24 GB cards | | Dynamic Q4\_K\_XL build | A higher-quality text build aimed at 32 GB cards | | Perception encoder | Required for image input | | DFlash drafter | Optional, for speculative decoding | ### Example You start llama-server with the smaller text build and the encoder. You ask a short arithmetic question. The reply holds the answer in the content field, and the model's reasoning shows up separately. ### For newcomers and experts If you are new, this is a big AI model packed into files that llama.cpp can run on your own computer. Check your llama.cpp version first, then try the smaller text build alone. If you know the field, these are 4-bit K-quant builds of a 30 billion parameter model with a separate multimodal projector and a draft model for speculative decoding. The text builds work alone, and the two companion files are additive. Only quantized files are published. You install llama.cpp b10353 or newer → You download the text build and encoder → llama-server loads them on your GPU → You send chat requests to the server ### Good to know Memory use grows as you add the encoder and the drafter. Totals below include a working context. File sizes | Label | Value | | --- | --- | | 17 GB text build | 16.8 GB | | Dynamic text build | 19.7 GB | | Perception encoder | 1.4 GB | | DFlash drafter | 1.6 GB | Rough total memory | Build | Text only | With vision | With vision and drafter | | --- | --- | --- | --- | | Smaller build | about 17 GB | about 19 GB | about 20 GB | | Dynamic build | about 20 GB | about 22 GB | about 23 GB | - License: Apache 2.0 - Cost to run: none named, though you need a GPU with about 24 GB for the smaller build - Data it reaches: the model files from Hugging Face, and the chats you send to your local server - Not for: full precision, or llama.cpp builds numbered b10344 and older ## README ````markdown # Muse Glimmer 30B GGUF by meta-models GGUF builds of a 30-billion-parameter agentic model for llama.cpp, with an optional image encoder and a speculative decoding drafter. They suit people who run models locally on a 24 GB GPU. Made by meta-models 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 conversions of Muse Glimmer 30B for llama.cpp. The card calls the model a 30-billion-parameter language model with its own perception encoder, built for agent tasks on consumer hardware and meant to run locally with no cloud access. You get two quantized text builds, an image encoder file and an optional drafter file for speculative decoding. The card names Meta Superintelligence Lab as the authors. Who it suits: It suits people who want a large agent-style model running on their own machine through llama.cpp. A 24 GB GPU handles the smaller text build. ```table Title: Who might use it Industry | Who uses it | How they use it | Example Local AI | Hobbyists with a 24 GB GPU | Run a private assistant with no cloud calls | Serve the model on the same computer and chat with it Software | Agent developers | Point tool-using agents at a local server | Test multi-step tasks against a model you control Research | Multimodal tinkerers | Add image input with the perception encoder | Load the encoder file next to the text build ``` If you need full precision, you can skip it. No bf16 GGUF is published here, and the card sends you to the base repository for that. Where to use it: It runs in llama.cpp, using llama-server, llama-cli or llama-mtmd-cli. The card needs a build numbered b10353 or newer. - llama.cpp on Windows, macOS or Linux - A CUDA GPU, a CPU only, or Apple Metal, depending on how you build - An OpenAI-style chat endpoint served by llama-server ```table Title: Jobs it does Use case | Who | What they get back Local chat and reasoning | Anyone with a big GPU | Answers, with the thinking returned in a separate field Image questions | Multimodal users | Text answers about a picture, once the encoder file is loaded Faster generation | Power users | Speculative decoding with the drafter file ``` How to set it up: Get a recent llama.cpp first, because older builds cannot load these files at all. Then download the weights you need and start the server. - llama.cpp release b10353 or newer - About 17 GB of memory for the smaller text build, more with extras - Python with pip, for the Hugging Face tools 1. Download a llama.cpp release tagged b10353 or later, or build it from source. 2. Check the version number is at least 10353. 3. Download the text build and the encoder file. 4. Start llama-server with the model and the encoder. ```bash ./llama-cli --version # build number must be >= 10353 ``` ```bash cd llama.cpp cmake -B build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON cmake --build build --config Release -j \ --target llama-cli llama-mtmd-cli llama-server ``` ```bash pip install huggingface_hub hf download meta-models/Muse-Glimmer-30B-GGUF \ --local-dir Muse-Glimmer-30B-GGUF \ ``` Put your own value in its key setting first. How to use it: Run llama-server with the text build and the encoder file, then send chat requests. The answer arrives in the content field and the thinking in a separate reasoning field. To add the drafter, pass its file with the extra flags. ```bash ./build/bin/llama-server \ -m Muse-Glimmer-30B-GGUF/Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf \ --mmproj Muse-Glimmer-30B-GGUF/mmproj-Muse-Glimmer-30B-Q4_K_M.gguf \ -a muse-glimmer-30B \ -ngl 99 -c 131072 -np 4 \ --host 127.0.0.1 --port 8080 \ --jinja \ --temp 1.0 --top-p 0.95 --top-k 64 ``` ```bash -md Muse-Glimmer-30B-GGUF/dflash-Muse-Glimmer-30B-Q4_K_M.gguf -ngld 99 ``` ```table Title: The files Tool | What it does Q4_K_M text build | The main text model, the card's starting point for 24 GB cards Dynamic Q4_K_XL build | A higher-quality text build aimed at 32 GB cards Perception encoder | Required for image input DFlash drafter | Optional, for speculative decoding ``` Example: You start llama-server with the smaller text build and the encoder. You ask a short arithmetic question. The reply holds the answer in the content field, and the model's reasoning shows up separately. For newcomers and experts: If you are new, this is a big AI model packed into files that llama.cpp can run on your own computer. Check your llama.cpp version first, then try the smaller text build alone. If you know the field, these are 4-bit K-quant builds of a 30 billion parameter model with a separate multimodal projector and a draft model for speculative decoding. The text builds work alone, and the two companion files are additive. Only quantized files are published. ```flow You install llama.cpp b10353 or newer You download the text build and encoder llama-server loads them on your GPU You send chat requests to the server ``` Good to know: Memory use grows as you add the encoder and the drafter. Totals below include a working context. ```chart Title: File sizes 17 GB text build: 16.8 GB Dynamic text build: 19.7 GB Perception encoder: 1.4 GB DFlash drafter: 1.6 GB ``` ```table Title: Rough total memory Build | Text only | With vision | With vision and drafter Smaller build | about 17 GB | about 19 GB | about 20 GB Dynamic build | about 20 GB | about 22 GB | about 23 GB ``` - License: Apache 2.0 - Cost to run: none named, though you need a GPU with about 24 GB for the smaller build - Data it reaches: the model files from Hugging Face, and the chats you send to your local server - Not for: full precision, or llama.cpp builds numbered b10344 and older ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 27.9B - Weights: about 13.9 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 198,352 - Likes: 393 - License: apache-2.0 - Based on: meta-models/Muse-Glimmer-30B - Fine-tunes: 2 - Tags: gguf, image-text-to-text, conversational ## Links - Model: https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF - Files: https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download meta-models/Muse-Glimmer-30B-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [meta-models/Muse-Glimmer-30B-GGUF on Hugging Face](https://huggingface.co/meta-models/Muse-Glimmer-30B-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 | | --- | --- | --- | --- | --- | --- | | Muse Glimmer 30B GGUF by meta-models (this page) | 198,352 | 393 | 27.9B | apache-2.0 | 2026-10-20 | | [Qwen3.8-Flash-Next-GSQ-RCO-GGUF](https://mindket.com/p/ee9ed4a6-39c8-4eee-8958-cce1cd508fa6) | 6,963,596 | 1,229 | 177B | apache-2.0 | 2026-09-29 | | [gemma-4-12b-it-GGUF](https://mindket.com/p/5c808b31-349e-4069-ad4e-f80ffc3a1bee) | 2,534,530 | 963 | 11.9B | apache-2.0 | 2026-07-17 | | [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 | | [Qwen3.8-27B GGUF (ShapeLearn Quantized)](https://mindket.com/p/ca45abfc-d3db-4fb5-aad9-dcf7a7a498c4) | 2,083,628 | 216 | 27.3B | apache-2.0 | 2026-09-18 | | [Qwen3.5-9B-GGUF](https://mindket.com/p/0f08c237-de8d-4e85-a65e-69b9b6061be0) | 1,969,901 | 1,074 | 9B | apache-2.0 | 2026-03-02 | 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 · Unsloth AI · 2026-08-10](https://bsky.app/profile/unsloth.ai/post/3mspv7me4l222): Meta releases Muse Glimmer, a new 30B open model that runs on 18GB RAM. Apache 2.0 licensed and built for agentic coding, it supports vision and is the strongest agentic model for its size. Run and train the model via Unsloth. GGUF: huggingface.co/unsloth/Muse... Guide: unsloth.ai/docs/models/... - [Bluesky · Stuart Gray · 2026-08-10](https://bsky.app/profile/sgray.bsky.social/post/3mspwbyu3pc2v): Meta have just dropped Muse Glimmer 30B. As a dense model it’s a bit of a mixed bag with benchmarks suggesting it’s broadly comparable with Qwen 3.6 27B and Gemma 4 31B, and comes with control over reasoning effort. huggingface.co/meta-models/... - [Bluesky · Fabrice Aneche · 2026-08-10](https://bsky.app/profile/akhen.bsky.social/post/3msq4eccllc2z): Muse Glimmer on XTX7900 74tk/s with draft-dflash and kquant-17gb, promising so far. huggingface.co/meta-models/... ## 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: 2026-10-20 - First published: 2026-08-09 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open meta-models/Muse-Glimmer-30B-GGUF on Hugging Face](https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2dd183ee-1c55-44aa-9c16-c26b1ee436ef) - [Its page on Mindket](https://mindket.com/p/2dd183ee-1c55-44aa-9c16-c26b1ee436ef)