{"slug": "run-openclaw-with-muse-glimmer-locally-on-mac", "title": "Run OpenClaw with Muse Glimmer Locally on Mac", "summary": "Meta Superintelligence Labs released Muse Glimmer, its first open model, on August 10, 2026, as a 30B multimodal model with a 128K+ context window under Apache 2.0, designed for local agent workloads. OpenClaw users can run it locally on Apple silicon Macs with 32 GB or more unified memory, using Ollama or HolaClaw, with an M5 Pro or better chip recommended for responsive performance.", "body_md": "Muse Glimmer is Meta Superintelligence Labs' first open model, released on August 10, 2026. It's a 30B multimodal model with a 128K+ context window, published under Apache 2.0 and built for always-on local agent workloads. **That's perfect for OpenClaw, and running it locally means no API key, no per-token bill, and nothing leaving your Mac**.\n\nOpenClaw doesn't run models itself. It talks to an engine that serves one over a local endpoint, so to integrate Muse Glimmer you need to:\n\n- Serve the model on your Mac\n- Configure OpenClaw to use it\n\n**This guide covers different scenarios**, from running your own inference server to using [HolaClaw](/download), our native macOS application that installs and runs OpenClaw securely on your Mac.\n\n## What you need\n\nBefore you start.On base M3 and M4 MacBook Airs, expect minutes of waiting before you get a response from OpenClaw. We recommend checking the[Can your Mac run Muse Glimmer?]article.\n\n**An Apple-silicon Mac.** Muse Glimmer runs on the GPU through Metal, so M-series only.**32 GB of unified memory or more.** Muse Glimmer, at the context length OpenClaw needs, does not fit in less.**About 30 GB of free disk.** Around 17 GB of model weights, plus OpenClaw and the apps this guide installs.**M5 Pro or better chip.** Base M3 and M4 chips land around 4.3 tokens per second; an M5 Pro or better is where an assistant starts to feel responsive.**OpenClaw installed and running.** Only for options A and B below. If you don't have it yet, skip to[If you are starting from zero](#if-you-are-starting-from-zero).\n\nContext length is the setting that decides whether the model fits. Bigger context means more memory, and an OpenClaw assistant needs a good amount of it: the system prompt, tool definitions, and skills already fill about 19K tokens before you type anything.\n\n## How to run OpenClaw with Muse Glimmer?\n\nServe the model with an inference engine on your Mac, then configure OpenClaw to use its endpoint. We prepared different guides based on your current setup. You can jump directly to the section that best covers your use case.\n\n**You already run OpenClaw.** You need an engine serving Muse Glimmer. Use[Ollama](https://ollama.com)if you already have it, or[Llama](https://llama.app)for the setup we benchmarked.**You're starting from zero or you are experimenting.** Both OpenClaw and the model can be installed in a single step, either with`ollama launch openclaw`\n\nor with[HolaClaw](/download).\n\n## If you already run OpenClaw\n\n### Option A: serve it with Ollama\n\n**Install Ollama.** Download the application from[ollama.com](https://ollama.com)and open it.**Pull the model.** Muse Glimmer has been in the[Ollama library](https://ollama.com/library/muse-glimmer)since release day. Open a terminal and run:\n\n```\nollama pull muse-glimmer:30b-mlx\n```\n\n**Raise the context length.** In the Ollama application, open**Settings** and move**Context length** to**64k**. The default of 16k is below what an OpenClaw assistant needs before you even type.** Keep the Ollama application open.**It serves the model to your OpenClaw installation.** Configure OpenClaw to use Ollama.**Run the following command where your OpenClaw installation lives. If it's on the same Mac:\n\n```\nopenclaw onboard --non-interactive --accept-risk --skip-health \\\n  --auth-choice ollama \\\n  --custom-base-url \"http://localhost:11434\" \\\n  --custom-model-id \"muse-glimmer:30b-mlx\"\n```\n\nIf OpenClaw runs on a different machine, you first need to expose the Ollama service to the network. For that, open the Ollama application, go to\n\n**Settings**, and turn on** Expose Ollama to the network**.Then, run the following command in the machine that runs OpenClaw:\n\n```\nopenclaw onboard --non-interactive --accept-risk --skip-health \\\n  --auth-choice ollama \\\n  --custom-base-url \"http://OLLAMA_HOST:11434\" \\\n  --custom-model-id \"muse-glimmer:30b-mlx\"\n```\n\nYou have all the documentation about the\n\n[Ollama provider in the OpenClaw docs](https://docs.openclaw.ai/providers/ollama).**Check it works.** Start a conversation and wait for the first reply. On base hardware it will take a while.\n\nA note on engines: the numbers we publish come from llama.cpp with the GGUF build, which is what HolaClaw uses under the hood. Here we recommend using MLX instead, as it may give you some performance gains on Apple silicon.\n\n### Option B: serve it with Llama (llama.cpp)\n\nLlama is a Mac native application that uses llama.cpp, an inference engine, under the hood. It's more complex than Ollama, but it gives you more configuration and options, and the new Llama application makes it much easier to use than it used to be.\n\n**Install Llama.** Download the application from[llama.app](https://llama.app), or install it with`brew install --cask llama-app`\n\n.**Pull the model and serve it.** You can use the`llama`\n\nCLI tool to install and serve the official[Muse-Glimmer-30B-GGUF](https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/tree/main)model. The`kquant-17gb`\n\nvariant requires about 17 GB on disk. To install it, open a terminal and run:\n\n```\nllama serve -hf meta-models/Muse-Glimmer-30B-GGUF:kquant \\\n  -ngl 99 \\\n  -c 65536 \\\n  --flash-attn on -b 2048 -ub 512 \\\n  --load-mode mlock --keep 512 \\\n  --cache-type-k q4_0 --cache-type-v q4_0 \\\n  --jinja --reasoning off \\\n  --port 8080\n```\n\n`-c`\n\nis the context length. This example serves 64K, which is what a 32 GB Mac holds. The KV cache is quantized to`q4_0`\n\n, which is part of why a context that large fits at all.**Keep the server running.** It serves the model to your OpenClaw installation, so leave that terminal open.**Update OpenClaw configuration to use Llama server.** Llama exposes an OpenAI-compatible API, so you need to configure it as a custom provider. For that, edit the`openclaw.json`\n\nfile (usually available at`~/.openclaw/openclaw.json`\n\n) to configure Llama. Here you have an example configuration, but you might need to adapt it to your setup:\n\n```\n{\n  \"agents\": {\n    \"defaults\": {\n      \"model\": { \"primary\": \"local/muse-glimmer-30B\" }\n    }\n  },\n  \"models\": {\n    \"mode\": \"merge\",\n    \"providers\": {\n      \"local\": {\n        \"baseUrl\": \"http://127.0.0.1:8080/v1\",\n        \"apiKey\": \"sk-local\",\n        \"api\": \"openai-completions\",\n        \"timeoutSeconds\": 300,\n        \"models\": [\n          {\n            \"id\": \"muse-glimmer-30B\",\n            \"name\": \"Muse Glimmer\",\n            \"reasoning\": false,\n            \"input\": [\"text\"],\n            \"cost\": { \"input\": 0, \"output\": 0, \"cacheRead\": 0, \"cacheWrite\": 0 },\n            \"contextWindow\": 65536,\n            \"maxTokens\": 8192\n          }\n        ]\n      }\n    }\n  }\n}\n```\n\nIf you are running the Llama app on a different machine, update\n\n`baseUrl`\n\nto use the right IP address.**Check it works.** Restart OpenClaw, start a conversation, and wait for the first reply. On base hardware it will take a while.\n\n### If something doesn't work\n\n**The model doesn't load, or your Mac starts swapping.** The context is too large for your memory. Lower`-c`\n\nand try again.**Tool calls time out.** At 4 tokens per second a single turn can run for minutes, past the default timeouts in some tools. Raise them before assuming something is broken.**The conversation stops working after a few turns.** History plus tools has overflowed the context you served with. Start a new conversation, or serve a larger context on a machine with the memory for it.**OpenClaw can't reach the endpoint.** Confirm the server is up with`curl http://127.0.0.1:8080/v1/models`\n\n, and check nothing else has taken the port.\n\n## If you are starting from zero\n\nTwo routes install OpenClaw and wire up a model in one step.\n\n### Option C: Ollama Launch\n\n`ollama launch`\n\nis a command that installs OpenClaw on your current machine and configures the provider and the model for you. Ollama documents it in their [OpenClaw integration guide](https://docs.ollama.com/integrations/openclaw).\n\n**Install Ollama.** Download the application from[ollama.com](https://ollama.com)and open it.**Launch OpenClaw with Muse Glimmer.** Open a terminal and run:\n\n```\nollama launch openclaw --model muse-glimmer\n```\n\n**Read the security notice and accept it.** Ollama explains the risks the first time you launch, and asks you to acknowledge them before continuing.**Check it works.** Start a conversation and wait for the first reply. On base hardware it will take a while.\n\nThis command will **install OpenClaw directly on your Mac with your user's permissions**. This solution is pretty straightforward, but **comes with some security concerns**. The agent has access to your device.\n\nTo avoid these security concerns, check the HolaClaw option below.\n\n### Option D: HolaClaw\n\nHolaClaw is our Mac app. One click installs OpenClaw inside an isolated virtual machine with secure defaults, and handles the model download, the inference server, updates, and backups. There's no account and no terminal.\n\nMuse Glimmer ships in the local model catalog from [v1.5.0](/changelog), served with llama.cpp under the hood. It needs 32 GB of unified memory.\n\n**Install HolaClaw.** Download the[HolaClaw app](/download)and open it.**Start the Create Assistant flow.** Click the**Get Started** button.**Pick the model.** In**Step 2 · Model provider**, choose** Local**and select** Muse Glimmer 30B**. The row shows the download size and the memory it needs.** Finish creating the assistant.**HolaClaw sets up the assistant and downloads the weights the first time it needs them, which takes a while on a first run.**Start chatting.** HolaClaw starts the inference server for you.\n\nThe hardware guidance above still applies here. HolaClaw removes the setup work, not the wait: the model runs at the speed your Mac can run it.\n\n[Download HolaClaw](/download) to try it.\n\n## Questions people ask\n\n**Can I run Muse Glimmer on a 16 GB Mac?**\nNo. Powering OpenClaw with Muse Glimmer needs 32 GB of unified memory. On a 16 GB Mac, pick a smaller local model instead: the [local models guide](/docs/ai-providers/local-models) lists what fits.\n\n**Is Muse Glimmer good for OpenClaw agent tasks?**\nThis model is designed for agentic work and tool calling. It's ideal if you want to run OpenClaw locally and your hardware can run it at a decent speed.\n\n**Does it work offline?**\nYes. Once the weights are on disk, everything runs on your Mac with no internet connection and no account.\n\n**Is it free?**\nYes. Muse Glimmer is released under Apache 2.0, and running it on your own hardware costs nothing.\n\n## Related\n\n[Can your Mac run Muse Glimmer?](/blog/muse-glimmer-on-mac), the benchmark behind the numbers in this guide.[Local models](/docs/ai-providers/local-models), the full catalog of models HolaClaw runs on your Mac.[Choosing an AI provider](/docs/ai-providers/choosing-a-provider), if you're weighing local against the cloud.\n\nRunning Muse Glimmer on a Mac we haven't tested? Come tell us how it went in our [Discord](https://discord.gg/FbxAbS5sGQ).", "url": "https://wpnews.pro/news/run-openclaw-with-muse-glimmer-locally-on-mac", "canonical_source": "https://holaclaw.ai/docs/tutorials/use-muse-glimmer-with-openclaw", "published_at": "2026-08-12 15:52:11+00:00", "updated_at": "2026-08-12 16:14:49.506903+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-tools", "ai-products"], "entities": ["Meta Superintelligence Labs", "Muse Glimmer", "OpenClaw", "Ollama", "HolaClaw", "Apple"], "alternates": {"html": "https://wpnews.pro/news/run-openclaw-with-muse-glimmer-locally-on-mac", "markdown": "https://wpnews.pro/news/run-openclaw-with-muse-glimmer-locally-on-mac.md", "text": "https://wpnews.pro/news/run-openclaw-with-muse-glimmer-locally-on-mac.txt", "jsonld": "https://wpnews.pro/news/run-openclaw-with-muse-glimmer-locally-on-mac.jsonld"}}