Can your Mac run Muse Glimmer? We tested it | HolaClaw Blog
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All articles Article<br>Can your Mac run Muse Glimmer? We tested it<br>Yes, Muse Glimmer runs on a MacBook. We tested Meta's new 30B open model on base M3 and M4 machines: it fits in 24GB of RAM but generates at 4.3 tokens per second. The numbers, why it's slow, and what hardware makes it usable.<br>Angel M Miguel Co-founder 路 August 11, 2026 路 7 min read
Meta Superintelligence Labs released Muse Glimmer on August 10, 2026. It's a 30B multimodal model with a 128K+ context window, released under Apache 2.0, and built for always-on local agent workloads.
The release is not a one-off. In The future is for everyone, Mark Zuckerberg lays out Meta's position that you should be able to run and own your personal AI intelligence . Muse Glimmer is the first model they've shipped against that idea: open weights, a permissive license, and a size aimed at consumer hardware rather than a datacenter.
At HolaClaw, we believe local models and AI assistants will be part of the future . For you and your business. That's why we always love to test and integrate new models that you can run on your own hardware. But, can you run Muse Glimmer on your Mac? What performance can you expect, and what's the minimum hardware you need so it's usable?
What we tested on
We tested Muse Glimmer on two MacBooks, both the base consumer tier:
MacBook Air M3, 24 GB of unified memory
MacBook Air M4, 32 GB of unified memory
Both are MacBook Airs, which means both are fanless . That detail matters more than it sounds; we'll come back to it.
We ran the model with llama.cpp, the same inference engine HolaClaw uses under the hood, using the official Muse-Glimmer-30B-GGUF build in the kquant-17gb variant (about 17 GB on disk). These are the llama-server flags we used:
llama-server -m Muse-Glimmer-30B-kquant-17gb.gguf \<br>-ngl 99 -c 8192 \<br>--flash-attn on -b 2048 -ub 512 \<br>--mlock --keep 512 \<br>--cache-type-k q4_0 --cache-type-v q4_0 \<br>--jinja --reasoning off
That's the 24 GB machine's configuration. On the 32 GB machine we raised the context to 64K (-c 65536). The tests are text-only, without the vision projector (--mmproj), leaving that RAM for the model and its context. Worth noting: the KV cache is quantized to q4_0, which is part of why contexts this large fit at all.
We plan to test the DFlash variant and the MLX format in a follow-up.
What we found
Muse Glimmer runs on a base MacBook Air . Daily usage is a different story.
Running it
The model fits in 24 GB of RAM at an 8K context and generates correct output. That's a 30B multimodal model working on a fanless consumer laptop.
Memory is what decides how far you can push it: 24 GB fits the model but not large contexts (64K did not fit on that machine), so 32 GB is our practical minimum.
Using it every day
Generation runs at about 4.3 tokens per second , pretty slow.
The bigger number is the wait before the answer starts: on a prompt of about 4,000 tokens, the first word appeared after roughly 157 seconds. For casual chat, that's below comfortable reading speed. For interactive work like coding, it's not enough.
Better hardware helps here, because generation speed follows memory bandwidth, and the Pro and Max chips have several times more of it than the base machines we tested. More on that at the end of the post.
Can you use it with OpenClaw on your Mac?
Yes, but only on high-end Macs . An OpenClaw assistant never sends your message alone: before your first word, the context already carries the system prompt, tool definitions, and skills, around 19K tokens in a fresh setup. At the prefill speed we measured, a base chip spends around 12 minutes reading that before the first reply starts. In HolaClaw, we reduced the initial context to ~14K, but it's still high for Muse Glimmer on a MacBook Air.
Our recommendation : 32 GB of unified memory as the minimum requirement for Muse Glimmer, and an M5 Pro or better if you want assistant-grade speed.
If you have one of these high-end Macs, you can try it in HolaClaw from v1.5.0.
The numbers we measured
Two phases matter when a model answers you, and they behave very differently:
Prefill is the model reading your prompt: everything you send, from your question to the conversation so far, gets processed before the model can say anything, and this determines how long you wait for the first word.
Generation is the model writing the answer, and it determines how fast the words come out once they start. A model can be acceptable at one and frustrating at the other, which is exactly what happens here.
How we tested
We measured the two phases with two kinds of requests:
Short prompts. One-line questions with short answers. Almost all the time goes to writing the reply, so this isolates generation speed.
A long prompt. A roughly 3,000-word article (4,166 tokens) the model has to summarize. Here the model spends most of its time reading before it writes...