ReMyndcan you run muse spark open on a mac?
as of october 2026, yes, but not on a laptop. on october 15, 2026, meta released muse spark open open weights, an 816b total / 42b active multimodal moe with a 1m token context. meta says it is the first model in its class to run on a desktop with two apple m5 ultra mac studios and mlx.
so can you run muse spark open on a mac? with the dual or quad muse spark open mac studio stack meta documents, yes. on a macbook or a single lighter studio, no. everyone else should use a hosted api or a smaller open model.
our verdict: treat muse spark open as a desk cluster or api model. keep muse glimmer for a normal mac, and keep your working history somewhere portable.
what did meta ship that day?
weights, not a new consumer chat app. meta's research post says the release is built for coding and agentic work, is natively multimodal, and is available through api platforms at baseten, fireworks ai, groq, modal, nebius, openrouter and together ai. weights are listed on amazon sagemaker, google cloud model garden, hugging face and microsoft foundry models.
the hugging face muse spark open collection model card lists text, image, video and audio input with text output. pytorch's same day post adds that the open weights version shares the same base model as muse spark 1.3 and ships with extra safety post training for standalone use. that post also cites helion kernel speedups of 1.03x to 3.95x on nvidia and amd gpus, and a 3% end to end speedup on amd mi350.
this is a different product from muse wonder, which we covered as meta's closed api model on october 13.
what are the muse spark open requirements on apple silicon?
meta's supported apple recipe is narrow. the hardware platforms page lists apple as mac studio with m5 ultra at 2 x 512 gb or 4 x 256 gb of unified memory, running the nvfp4 checkpoint on muse spark open mlx. the mac cookbook adds the rest of the muse spark open requirements:
- macos 26.2 or later, for rdma over thunderbolt
- a direct thunderbolt 5 link between every pair of macs
- mlx 0.30.1 or later
- disk space for the full nvfp4 checkpoint on each mac
- about four hours to complete the setup as written
meta says the model does not fit in one mac's memory. mlx.launch starts the same mlx-lm program on every machine over ssh, and the macs exchange intermediate results over rdma on thunderbolt 5. that is a small cluster on your desk, not a one click download on a macbook pro.
for gpu servers, the model card is blunt: at least 700 gb of vram for nvfp4 and 2 tb for bf16. meta's get the model page puts the bf16 weight footprint at about 1.65 tb and nvfp4 at about 537 gb, before kv cache and runtime overhead. that same page lists bf16 on 8 x b300 and nvfp4 on 4 x b300.
how does muse spark open compare to models that already fit a mac?
use this table as a sizing check, not a quality ranking. the local rows come from our best local llm for mac guide, still the best open weight model mac baseline for machines under 96 gb. the spark open row is meta's own apple recipe above.
| model | what you need | params (total / active) | license notes | our pick for |
|---|---|---|---|---|
| muse glimmer | one 24 or 32 gb mac | 30b dense | apache 2.0 | normal local agents |
| gemma 4 12b | one 16 gb mac | 12b dense | apache 2.0 | everyday local chat |
| qwen3.8-27b | one 24 to 36 gb mac | 27b dense | apache 2.0 | local coding |
| muse spark open (mlx) | meta's dual or quad studio recipe | see lead | muse model license | on desk frontier agents |
| muse spark open (api) | a partner api key | see lead | partner terms | try it without buying studios |
meta's own benchmark table on the model card puts the new open weights at 39 on the artificial analysis intelligence index (xhigh), 61.8 on jobbench, 89.2 on mcp-atlas and 76.4 on terminal-bench 2.1 with the terminus 2 agent. several peer open models beat it on some rows. kimi k3, for example, leads terminal-bench 2.1 at 85.0 and deepswe 1.1 at 68 in that same table. treat those as meta's published numbers, not our own runs.
on safety, the model card says the checkpoint refused 99.2% of jailbreaks on meta's internal cyber benchmark and 94.8% when aggregating polyguard cyber, redcode and cyberbench 2024. the same card reports over 98% refusal on securebio's biotier refusal benchmark. those figures are for the released checkpoint under the tested conditions.
is the muse model license actually free?
for many developers, yes, with strings. the license text on hugging face grants broad rights to use, modify, fine tune and distribute, subject to conditions. two matter in practice:
- credit. you must prominently display "built with muse spark open" in the product ui, docs, site or settings.
- model as a service over $20 million. if you and your affiliates offer ai models as a product or service over an api (or similar), and aggregate gross revenue exceeds that figure in any consecutive twelve month period, you must notify meta and enter a separate written agreement before offering the software or derivatives in that model as a service business. internal use and non maas products are carved out.
meta's research post summarizes this as the muse model license with limited commercial terms, which allow many developers to use the model for free. read the full text before you ship a hosted product on top of it. apache 2.0 models in our local mac guide do not carry that revenue trigger.
should you pick muse glimmer vs muse spark open?
only switch if you already have the hardware, or you are fine paying a host. in our view, as of today:
- stay on muse glimmer (or gemma 4 / qwen3.8) if your mac is in the memory bands those models were sized for in our september 24, 2026 guide. nothing in meta's apple recipe changes a laptop.
- try a hosted spark open endpoint if you want the agentic coding push without buying studios. meta named the seven api partners on day one.
- buy the dual studio stack only if you need the weights on your own desk for privacy, air gap or fine tuning reasons, and you can meet the cookbook's os, thunderbolt and nvfp4 disk cost on every machine.
- do not confuse it with muse wonder. wonder is the closed model; spark open is the downloadable weights. pick with the access model you actually want.
for the wider closed model ranking, see claude vs chatgpt vs gemini and best ai model for coding. for long context sizing on any of these, start with what is a context window. apple's own vision docs are still the right reference for on device ocr when you keep an archive next to these models.
keep your context when the open weights change
our take: open weight releases will keep jumping in size, and your week of work should not live inside any one checkpoint's chat history.
remynd is a mac app that keeps that record for you. it captures the focused window, runs ocr locally with apple vision, and keeps the searchable index on your mac. you can exclude apps and sites, and recordings are kept for 30 days by default.
its agent window runs your own claude code or codex cli against that history. call transcription already runs on device with mlx, and settings accept a custom openai responses compatible api endpoint, so pointing an experiment at a local server is possible even when we have not published a tested spark open recipe.
download remynd for mac and keep your memory when you change models.