How to Run Muse Glimmer Locally with Ollama (2026 Guide)

What Muse Glimmer Is and Which Tag to Run

Muse Glimmer is a 30 billion parameter causal language model distilled from Meta's larger Muse Spark, with a dedicated perception encoder for image understanding. It's built for agentic work specifically, tasks where a model has to plan multiple steps, call tools with the right arguments, and keep going when something breaks partway through, rather than a general chat model that happens to also do those things.

Meta benchmarked it against Gemma4-31B and Qwen3.6-27B, two models in the same size class, across general agentic tasks, agentic coding, multimodal understanding, and safety. On MCP Atlas, a public benchmark for tool-using agents, Muse Glimmer scored 75.5 against Gemma4-31B's 54.2 and Qwen3.6-27B's 62.5. On SWE-Bench Verified, a benchmark for resolving real GitHub issues, it scored 76.0, ahead of Gemma4-31B's 66.6 and just behind Qwen3.6-27B's 77.2. No model sweeps every category here, which is the normal pattern at this size class, so check the benchmark closest to what you'll actually be doing rather than trusting a single score.

On Ollama's library, the model ships with two real tags:

The plain `muse-glimmer` tag, the explicit `30b` tag, and `latest` all point at the identical 18GB download. Muse Glimmer's Ollama library page lists just that one standard build plus the Apple Silicon MLX variant, unlike some local models that ship several quantization levels: pull the default tag on Linux, Windows, or Intel Macs, and the MLX tag on Apple Silicon.

Install Ollama and Run Your First Muse Glimmer Prompt

Getting Muse Glimmer running takes about ten minutes on a normal connection, most of it spent downloading the 18GB tag.

Step 1: Install Ollama

# Linux and macOS, one-command installer
curl -fsSL https://ollama.com/install.sh | sh

On Windows, download the installer from ollama.com/download, or use winget:

winget install Ollama.Ollama

Confirm Ollama is on its latest release, since Muse Glimmer was added recently:

ollama --version

Step 2: Pull and Run muse-glimmer

ollama run muse-glimmer

Expected output on first run:

pulling manifest
pulling 7c19a4f2... 100% ▕████████████████▏  18 GB
pulling tokenizer...   100% ▕████████████████▏ 3.8 MB
success
>>> Send a message (/? for help)

On an Apple Silicon Mac, pull the MLX-optimized tag instead. Ollama's MLX engine is tuned for that hardware and adds image input support:

ollama run muse-glimmer:30b-mlx

Step 3: Send a Test Prompt

>>> Write a Python function that checks if a string is a valid IPv4 address, then explain the edge cases it handles.

Muse Glimmer is tuned for agentic and coding tasks, so expect a direct, structured response. The first response takes longer than later ones while the model and its context load fully into memory.

Step 4: Verify the Model

ollama list

`muse-glimmer:latest` appears in the list at its full download size, 18 GB. This confirms the weights are actually on disk, not only cached for a cloud call.

Using Vision Input, Tool Use, and Controllable Reasoning

Muse Glimmer's three defining features, image understanding, reliable tool calling, and adjustable reasoning depth, are all reachable from the command line and the API.

Send an Image from the Terminal

ollama run muse-glimmer "What does this chart show? ./quarterly-revenue.png"

The perception encoder reads the image and reasons over it alongside the text prompt in the same request, useful for an agent that needs to interpret a screenshot, chart, or document mid-task.

Control Reasoning Strength via the API

curl http://localhost:11434/api/chat -d '{
  "model": "muse-glimmer",
  "messages": [
    { "role": "user", "content": "Plan a multi-step migration from a monolith to microservices for this codebase." }
  ],
  "think": "high",
  "stream": false
}'

Muse Glimmer supports four reasoning strength levels: `low`, `medium`, `high`, and `xhigh`. Lower levels trade reasoning depth for faster, cheaper responses on simpler prompts, while `xhigh` is built for the longest, most complex agentic workflows.

Tool Use and Failure Recovery

Muse Glimmer calls tools with precise schema handling across extended workflows, and diagnoses and retries when a tool call fails or returns something unexpected instead of stopping. That's the actual thing its MCP Atlas and SWE-Bench Pro scores are measuring: whether it can carry a multi-step task through to a working result, not just answer one question well.

Connect Muse Glimmer to an Agent Scaffold

Muse Glimmer is designed to sit inside an agent scaffold, software that gives the model a loop, a set of tools, and a task to run end to end, not just answer prompts one at a time. Ollama ships a launch shortcut for this that skips the manual config step.

The ollama launch Shortcut

ollama launch hermes --model muse-glimmer

ollama launch openclaw --model muse-glimmer

Both commands start the named scaffold, Hermes Agent or OpenClaw, pre-configured to use Muse Glimmer as its model, instead of requiring a separate config file edit. Ollama's library also lists launch shortcuts for Claude Code and OpenCode using the same `--model muse-glimmer` pattern.

Manual OpenAI-Compatible Endpoint

If your scaffold or agent tool doesn't support the launch shortcut, point its OpenAI-compatible configuration at Ollama directly:

model:
  default: muse-glimmer
  provider: custom
  base_url: http://localhost:11434/v1
  context_length: 131072

Troubleshooting

Frequently Asked Questions