Comment by armcat
4 days ago
They've done considerable effort in making sure this model can run on basically every model runner, from Unsloth to MLX, https://dev.meta.ai/docs/muse-spark-open/deploy
4 days ago
They've done considerable effort in making sure this model can run on basically every model runner, from Unsloth to MLX, https://dev.meta.ai/docs/muse-spark-open/deploy
This is what stood out to me. They did the actual work of testing and documenting deployments on several platforms. It's a bit of upping the standard for open weights instead of just posting the blob and letting the community figure it out.
It makes sense from a provider standpoint. If you are going to give it away for free what is the end game? Helping build the world around your model such that it is a pathway that leads to greater adoption of your ecosystem is smart. Dev relations in a way.
Yes you need big compute to deploy this locally but $20-30K in hardware for specialty markets is nothing.
This isn't an accident. It is revealing a strategy.
As someone not in the loop, can you explain more of what you mean.
Apple research has also posted information on the topic.
> In this post, we show how to run the latest frontier-class model — Muse Spark Open, with nearly a trillion parameters — right from your desk, using the latest version of MLX, released today. On a cluster of four Mac Studios, Muse Spark Open generates over 36 tokens per second
Beginning on a single Mac Studio (up to 512GB of unified memory), then scaling out across multiple Macs with the RDMA over Thunderbolt driver, below we share an overview of the range of models you can now run on Mac Studio.
https://machinelearning.apple.com/research/mlx-distributed-i...
day one support for all inference engines so that whichever kind of capable computer you have, you can run it. you do need a really capable computer though
> you do need a really capable computer though
They suggest a pair of Mac Studios w/ M5 Ultra chips and 512 GB of RAM. A single Mac Studio w/ M5 Ultra and half that RAM (256 GB) is around $11k, and the 512 GB option isn't even available yet. So.... yeah.
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Anyone who has tried a smaller/quantized version locally on apple silicon? Would love to know your experience, also tk/s on what hardware specs?
Yes, but still, what about users looking for NPU support. What happened to Microsoft's copilot+ PC campaign? (the NPU hardware, not the logo key and ad campaign) AMD's XDNA2 NPU is best supported by ONNX, I think Qualcomm Hexagon is too. The best consumer unified architectures are Apple, AMD ai+ and Qualcomm Snapdragon X Elite, even intel has an NPU offering.
NPUs are meant for tiny, tiny models that can run basically at all times but cannot affect battery life (e.g. translation, live-video cleanup, filesearch etc.), not for big LLMs. This is about 500 times to large for a NPU.
NPUs you can find in AMD high-end CPUs are closer to specialized DSPs (basically big VLIW/SIMD vector units).
People love doing heavy math stuff with them (https://destevez.net/2026/05/getting-peak-tops-on-a-ryzen-ai... - not my blog)
There are plenty of smaller less powerful open weights models if all you have to run it on is an NPU.
This is a much larger more powerful model, so running it locally will not be for everyone, but you can still run it via a hosting service such as Fireworks AI.
I was mistaken in my thinking that open models are able to be quantized to run on average consumer hardware, I think they have outgrown that concept.
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Copilot+ PC is such a terrible and wasteful marketing effort that Microsoft has given it up.
NPUs are not inherently useless, but definitely so the way they are forced onto users machines.