🧠 Meta Muse Glimmer

Muse Glimmer: Download, Install and Run Meta’s AI Locally

Q: What is the easiest way to install Muse Glimmer?

For most beginners, install Ollama, then run ollama run muse-glimmer. If you prefer a graphical interface, LM Studio supports Muse Glimmer through its model catalog.

Muse Glimmer is Meta’s 30-billion-parameter open-weight model for agent workflows, tool use and image understanding. This guide shows how to run it with Ollama or LM Studio, how to estimate whether your hardware can handle it, and what local execution does and does not guarantee.

Updated October 14, 2026. Built by R.K., Creator and Business Economics Analyst.

Model tags, runtime support and hardware guidance change quickly. Everything below was checked against Meta’s model card and the Ollama and LM Studio pages on October 14, 2026. Re-check the linked official pages before you download a large file.

Quick answer

Install the latest Ollama, open a terminal and run ollama run muse-glimmer. The command downloads the model (about 18 GB for the common quantized build) and starts a chat session. Muse Glimmer is a 30B open-weight model released under Apache 2.0 that accepts text and images. Plan for roughly 24 to 32 GB of GPU or unified memory for a comfortable quantized setup. LM Studio lists 26 GB as its minimum system memory. Local does not mean fast on every laptop, and it does not mean private if your agent tool sends data elsewhere.

30BParameters, open weights

Apache 2.0License for model artifacts

About 18 GBDefault Ollama download

26 GBLM Studio minimum system memory

What is Meta Muse Glimmer?

Built for agents

Multi-step planning, function calls, coding workflows and recovery from failed steps.

Reads images

Screenshots, charts and document pages work as input when the runtime supports images.

Open weights

Download and run supported builds locally. Read the license and model card before commercial use.

Not the same as the Muse app. Muse Glimmer is not the consumer Muse assistant, and it is not Muse Wonder. Glimmer is a downloadable model you run with a compatible local runtime or through a supported provider. For the consumer product, read our Meta Muse AI features and tasks guide.

Primary sources: Meta’s announcement and the official model card on Hugging Face.

Muse Glimmer hardware requirements: RAM vs GPU memory

Do not treat the file size as the total memory requirement. The common quantized Ollama package is about 18 GB on disk, but the model also needs working memory for the context cache, the image encoder and the runtime. Long context or several simultaneous users raise memory use further.

Setup or build	Published size or target	What that means
Ollama default model	About 18 GB download; 128K context listing	Leave extra free disk space and memory beyond the download. The footprint depends on tag and runtime.
Meta K-Quant 17GB build	Target hardware: 24 GB VRAM	Meta’s stated target for this build. Context, images and other running apps eat into the headroom.
Meta K-Quant Dynamic	Target hardware: 32 GB VRAM	A higher-memory quantized option. Check the artifact and runtime instructions before downloading.
Full precision (BF16)	Weights around 60 GB; over 55 GB memory in Meta’s overview	Not a sensible start on consumer machines. It is meant for high-memory hardware and advanced deployments.
LM Studio catalog	Minimum system memory listed: 26 GB	This is LM Studio’s guidance, not a promise of speed or compatibility on every machine.

Practical read: a machine with 24 to 32 GB of suitable GPU or unified memory is a plausible starting point for the right quantized build, with no speed guarantee. With only 8 to 16 GB of total memory and no capable GPU, pick a smaller model or try a hosted option before downloading a large file.

Processor, GPU or Apple Silicon memory, quantization, context size, thermal limits and runtime all affect real speed. If you plan to send images, use long prompts or run many agent steps, leave memory headroom instead of sizing only for the weights.

Will Muse Glimmer run on your computer?

Use this as a first filter, not a compatibility test. It compares the memory you enter against published guidance and does not inspect your machine.

Hardware readiness checker

Total system or unified memory (GB)

Dedicated GPU VRAM (GB)

Computer type

Main use

Likely starting point: test a quantized build.

With the example values, try Ollama or LM Studio first. Confirm the runtime’s current requirements and watch memory use during a real task.

Rule of thumb only. It does not benchmark your CPU or GPU, cover every quantization, or prove a workload will fit. Apple unified memory and discrete GPU VRAM are not interchangeable in every runtime.

How to download and run Muse Glimmer with Ollama

Best for: a fast command-line start, local model serving and connecting supported agent tools. Use the latest Ollama version before running these commands.

Install Ollama.
Download it from Ollama’s official download page for your operating system and finish setup.
Open Terminal, PowerShell or your shell.
If the shell says the command is not found, reopen the terminal or check the installation.
Download and start the model.
ollama run muse-glimmer

The command pulls the model if it is not already on disk, then opens an interactive session. The first download is large, so use a stable connection and keep disk space free.
Try a low-risk test prompt.
Explain this Python function step by step. Identify one possible edge case, then suggest a unit test. Do not modify any files.
Start with a harmless question before you connect the model to tools that can read, change or run files.

Optional: the Apple Silicon MLX variant

Ollama lists a separate MLX variant for Apple Silicon. Confirm compatibility on the latest model page, then run:

ollama run muse-glimmer:30b-mlx

Tags and optimizations change. If the command fails, copy the current command from the Ollama model page instead of assuming every tag works on every OS.

Optional: connect a coding or agent tool

Ollama documents integration commands for several agent apps. Install and understand the target app first. The published command for OpenCode is:

ollama launch opencode --model muse-glimmer

Permissions matter. Agent tools can read files, run commands or make changes depending on their settings. Start in a disposable project folder, review permission prompts and back up anything important.

Install Muse Glimmer with LM Studio (graphical interface)

Other ways to use Muse Glimmer

Route	Good fit	Trade-off
Meta’s Hugging Face repository	Developers who need model files, the model card, architecture details or fine-tuning	More technical. Pick the right weights and runtime instead of downloading everything.
llama.cpp or a compatible GGUF build	Advanced users who want control over quantization, context and inference	Support depends on the current build, model format and version.
Hosted inference providers listed by Meta	Testing the model before buying hardware	Prompts may go to an outside service. Check its terms and privacy settings.

Meta’s launch post named partners and runtimes including Ollama, LM Studio, Unsloth, llama.cpp, MLX, ExecuTorch, vLLM, SGLang, Together AI, Fireworks AI and OpenRouter. Availability varies by provider, region, model revision and plan, so verify before you build a dependency on one endpoint.

What is Muse Glimmer good for?

Local coding help

Explain code, draft a patch, propose tests or help an agent work through a repository. Inspect every change before applying it.

Tool-using agents

Multi-step workflows that call structured tools, read the results and recover from unexpected responses.

Documents and screenshots

Read charts, screenshots and document pages when the chosen runtime passes images through.

The model card also lists synthetic-data generation and LLM-as-a-judge evaluation as intended uses. None of these is automatic. Agent results depend on the orchestration framework, tool descriptions, prompts, runtime settings and the safeguards around it.

Can a local model browse the web?

Not by itself. The weights generate answers from the prompt and the inputs you give them. Live web access or computer control needs a separate runtime, tool, connector or agent framework. Offline, the model cannot fetch live information unless you supply the data locally.

Muse Glimmer benchmarks: strengths and limits

Meta reports that Glimmer compares well with similar-size models on several agentic benchmarks. The selected results below show why your own use case matters more than one headline score.

Benchmark	Muse Glimmer 30B	Gemma 4 31B	Qwen 3.6 27B	What it tests
MCP Atlas	75.5	54.2	62.5	Multi-step tool and MCP orchestration
DeepSearch QA	74.6	61.7	71.1	Research and multi-step search tasks
SWE-Bench Verified	76.0	66.6	77.2	Repository-level software issues
OSWorld-Verified	65.9	58.5	75.6	Computer-use and GUI tasks
Terminal-Bench 2.1	51.7	43.4	60.7	Terminal-based agent workflows

Figures come from Meta’s model card. Scores are not on one scale, so compare within a row only. Results depend on the harness, agent scaffold, model variant and settings, and do not predict your real-world success rate.

In Meta’s own numbers, Glimmer leads on MCP Atlas and DeepSearch QA, while Qwen scores higher on SWE-Bench Verified, OSWorld-Verified and Terminal-Bench. Pick based on the work you need done, then test with your own tasks.

What independent testing says

Artificial Analysis’s launch evaluation rated Glimmer as capable for a 30B open-weight model. It also flagged weaknesses on agentic knowledge work and hallucination-related measures. Open weights and strong tool-use scores do not make a model accurate on every task.

Is Muse Glimmer open source? Can you use it commercially?

Meta released Muse Glimmer’s model artifacts under the Apache 2.0 license, including the listed weights and related artifacts. It is a permissive license widely used for commercial and research work. Read the license text and model card before you ship a product, and check the separate terms of every runtime, dataset, connector and dependency you use.

“Open weights” is the more precise label. It does not mean the whole training process or all training data is open, and it does not make the system safe for every deployment.

Is local Muse Glimmer private?

Local inference can keep prompts and files on your device when the whole workflow is local. Privacy depends on the setup around the model. A connected coding agent, plugin, telemetry setting, cloud fallback, browser tool or hosted API can send data elsewhere. Check network behavior, runtime settings, logs and permissions before you connect a private work folder.

Safety note: Meta advises using the model inside a system with proper protections, not as an unrestricted endpoint. Do not let an untested agent run arbitrary commands or make irreversible changes without review. The model card also says audio input and output are unsupported, and warns of inaccurate or biased output and multi-step reasoning errors.

Muse Glimmer troubleshooting

Download fails or says there is not enough disk space

Check free disk space before retrying. The common Ollama build is around 18 GB, and temporary download and cache files add overhead. Confirm the current tag size and remove only models you no longer need, using the documented commands for your tool.

The model loads but answers very slowly

Lower the context length, close memory-heavy apps, try a supported quantized variant and confirm the runtime uses the intended GPU or Apple Silicon acceleration. Long agent loops make many model calls, so delay adds up across steps. Compare speed only on matching hardware and settings.

The Ollama command is not recognized

Confirm installation finished, restart the terminal and check that Ollama is on your PATH. If it still fails, follow the official troubleshooting guide for your operating system.

The model ignores an image or screenshot

Confirm the model tag and runtime support image input, and use the runtime’s documented way to attach images. A text-only request or an incompatible integration may drop the image before it reaches the model.

A coding agent asks for access or tries to run commands

That is an agent-permission issue, not only a model question. Review the tool’s permissions, use a test project, inspect diffs and command previews, and keep destructive actions behind explicit confirmation.

Muse Glimmer FAQ

Is Muse Glimmer free to download?

The model weights are available to download under Apache 2.0. You still need compatible hardware and may pay for electricity, equipment or a hosted provider. Review current license terms and provider pricing.

What is the easiest way to install Muse Glimmer?

For most beginners, install Ollama, then run ollama run muse-glimmer. If you prefer a graphical interface, LM Studio supports Muse Glimmer through its model catalog.

How much RAM does Muse Glimmer need?

There is no single number because RAM, GPU VRAM, Apple unified memory, quantization and context length differ. LM Studio lists 26 GB minimum system memory. Meta targets 24 GB VRAM for its K-Quant 17GB artifact and 32 GB for K-Quant Dynamic. Verify the exact variant and runtime before installing.

Can Muse Glimmer run on a laptop?

Yes, on a laptop with enough memory. Meta says it is intended for local use on Mac or PC hardware, but ordinary laptops with limited memory may struggle with the larger quantized build. Use the hardware section above instead of relying on the word laptop.

Does Muse Glimmer work without internet?

Once the model is downloaded, local inference can run offline. Downloading updates, using web-search tools, hosted APIs or cloud-connected agent integrations needs a connection.

Can Muse Glimmer understand images?

Yes. The model includes a perception encoder and supports text plus image input. The runtime or integration you use must also pass image inputs correctly.

Does Muse Glimmer support audio or video?

The model card lists text and image input with text output, and audio input and output are not supported. It is not explicitly optimized for video, and video input is treated as individual frames, not native continuous-video understanding.

Can I use Muse Glimmer for commercial projects?

Meta releases the model artifacts under Apache 2.0, which generally permits commercial use subject to the license terms. Review the license, model card and every component in your deployment, and add safeguards that suit your product.

Is Muse Glimmer better than Qwen or Gemma?

Not for every task. Meta’s table shows Glimmer ahead on some agentic evaluations and behind Qwen on several coding and computer-use results. Benchmark setup matters, so run representative tests on your own workload before choosing.

Is Muse Glimmer the same as Muse Wonder or the Muse app?

No. Muse Glimmer is a downloadable open-weight model for local and developer workflows. Muse Wonder is a separate model family, and the Muse app is a consumer assistant. Do not treat them as interchangeable.

Bottom line: Muse Glimmer is worth testing when you want an open-weight local model for multi-step tool use, coding or image-aware agent work. Start with a quantized build, check memory needs, compare it on your own tasks and keep agent permissions under review. It is not automatically the fastest, safest or most accurate model for every workload.

How this page is built

Facts come from Meta’s announcement and model card, the Ollama and LM Studio pages, and independent analysis from Artificial Analysis. Where a figure depends on the runtime or build, the page says so instead of guessing. Last checked October 14, 2026.

Meta AI Research: Introducing Muse Glimmer (announcement, use cases, local deployment)
Muse Glimmer 30B model card on Hugging Face (architecture, benchmarks, variants, limits, license)
Ollama model library: Muse Glimmer (tags, sizes, commands)
LM Studio catalog: Muse Glimmer (memory guidance)
LM Studio local setup guide (install flow, local API examples)
Artificial Analysis: Muse Glimmer analysis (independent comparison)
Apache License 2.0 (license text)

Built and checked by R.K., Creator and Business Economics Analyst.

Disclaimer: This guide is for general information and is not legal, financial or security advice. Ultimate Info Guide is independent and is not affiliated with Meta, Ollama or LM Studio. Model tags, hardware guidance, integrations and benchmark results can change, so confirm on the official pages before you rely on this guide for deployment decisions.