https://dev.meta.ai/resources/blog/build-with-muse-spark-ope...
https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B
https://9to5mac.com/2026/10/15/meta-launches-new-muse-spark-...
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
re: "Apple’s unified memory architecture lets the M5 Ultra's integrated GPU use up to 512 GB per Mac Studio, so two machines can hold an entire model. And the chip’s 1.2 TB/s of memory bandwidth is fast enough to read the small slice each token needs. The result is two Mac Studios behave like one supercomputer with a terabyte of unified memory."
At the current market price of Apple Mac Studio Ultra, 30-core CPU + 64-core GPU w/ 256GB RAM is already reaching at around $10k. Keep in mind that computers are appreciating assets recently.
The 512GB RAM option is not even for sales yet (they said late October availability). Based on typical Apple pricing, it's likely to be around $14k apiece. So we are looking at least $28k - $30k for the setup Muse team is advertising. That's essentially the Nvidia H100 GPU w/ 96GB VRAM/HBM3 at up to 3.94 TB/second memory bandwidth, 3x+ than Mac Studio's Unified Memory speed.
Also, how did Meta get access to Apple's unreleased M5 Ultra w/512GB RAM Mac Studio? Or is that simply a marketing piece that they wrote purely on specs?
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Like I said last week. I think zucks original llama thesis is proving more correct by the week. The rent market on intelligence never materialized, if you have THE frontier model, you can charge rent (for a few weeks, maybe month). But after a certain capability level it's price that drives the decision.
Zuck is doing the thing he's really good at, and building a moat around the network (Muse is VERY sticky), and now that we're all going to standardize on Meta's model, we're going to optimize the heck out of the infra to run the model making it cheaper for him to scale, thus bringing in more participants to his network, and thus making his network more valuable.
Meta is going to win consumer AI.
If you’re falling for “sticky” interfaces, you’re not going make it. Your competitor won’t have the stickiness and the ability to use any harness and that will overcome Muse in the long-run. Let’s not forget, Muse is a close to SOTA model right now, but so was Google, and now they’re irrelevant. Google is one of many funded models that were close to SOTA, but we realized it’s benchmaxxed.
People are going to say that it doesn’t matter if you’re the best. If Muse, Grokbot, and Dot are able to reach mass appeal, it doesn’t matter if a person using a custom harness is better.
I respectfully disagree. There is a big difference between someone that’s running a custom harness that’s almost like a personal OS versus someone using something out of the box. And if you’re for using this to run your business, think for yourself who’s going to win, the person customizing everything or the one using what the big corps give you.
Google, Apple, and Microsoft are each in a stronger position to win consumer AI because they're guardians over the foundations of the user's experience. Meta always has to wonder whether they're going to be edged out, like when Apple decided to limit permissions or set new rules on user data handling.
How has Meta responded? With a failed phone and speculative headset.
Being one of if not the top advertisers in the world? I think Meta is doing just fine.
> How has Meta responded? With a failed phone and speculative headset.
I guess their spin on Muse would be, by building the AI agent that Apple has shown it's nowhere near being able to build
Apple responds by waiting and partnering with companies like Google, and treating this as a layer that can be swapped out.
>Google, Apple, and Microsoft are each in a stronger position to win consumer AI
Microsoft has completely lost the ability to make good software; see Microsoft Teams and what they've done to Windows 11. Apple don't have a foundation models team so they're limited to using other people's models, which handicaps them as they can't do vertical integration like with their other products. Google hasn't made a single competitive consumer product in the past decade, but has certainly killed a few.
>Google, Apple, and Microsoft are each in a stronger position to win consumer AI because they're guardians over the foundations of the user's experience.
From everything I've seen, they're all too large and dysfunctional to make a competent play in consumer AI. Anyone in there trying to work on it is too far removed from end product and is answering to too many masters.
> The rent market on intelligence never materialized
So where is the ~$100B combined revenue for OpenAI and Anthropic coming from? Open source models have been close enough to the frontier for over a year now, yet the $$ shows no sign of shifting.
So what makes Muse "very sticky"?
Seriously. Muse hasn't even been around long enough to determine if it is "sticky" in any meaningful way, let alone in the context of competing against frontier models from competitors.
And based on what I'm seeing, it's far from even being in that category as a service yet. Seems like the OP is conflating seeing a bunch of Muse ads on Facebook with its presence as a competing frontier model.
The ecosystem of meta products like Instagram
Unfounded assertions by marketing accounts, obviously.
Velcro
Good anthropomorphism.
> now that we're all going to standardize on Meta's model
Who's this "we" exactly?
definitely not me; I wouldn't doubt if Zster is working on turning muse into sleeper agents, _assuming_ chinese models will do the same, so they crib data and send it home like a good little maleware model.
In fact, we're going to see an explosion of the worst kind of AI model thanks to thinks mr Z thinks is in his best interest.
Remember, they had reports going back more than a decade about how they're harming children and destabilizing entire countries and as soon as Trumps in the whitehouse, they're licking boot.
> Muse is VERY sticky
How, exactly?
Sounds like cope. You can't claim the 'rent market on intelligence' never materialized when revenue is absolutely booming for Anthropic/etc.
Sure, just not nearly enough for sustainability I think. I'd reserve that call for the moment we can stop bathing in free VC money by using subscriptions.
Also, as far as my own use goes, I found that the capabilities/price ratio of DeepSeek's offering is very hard to beat. It's really good enough so the thing that matters to me (besides table stakes like uptime, latency, etc.) is price at this point.
They are just repeating old tropes about unprofitability without reassessing
Do you have a source for Anthropic being profitable?
Of course they are “profitable”, Dario said so last month (if you accept a definition of “profitability” under which every tech startup is profitable by the day they have their first paying customer).
If someone gives you one you're just going to say you don't believe it, so why bother?
The fact that Amodei had to invent a new definition of the word “profitable” last month is a strong hint…
How about the fact that they're not shouting from the rooftops how they're managing to make money in an industry that is currently mostly known as a giant money fire? Instead they're reporting being profitable "after making certain adjustments", which sounds a lot like "not profitable".
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The model license requires large inference providers to contact Meta separately to obtain a license to serve the model. I know we've seen this before with other large models, but it's still a bit irritating.
The usual effect of separate licensing is decreased price competition from public providers (like we saw with Kimi K3). I guess it's better for the providers if the price is fixed: any efficiency improvements directly improve margin rather than driving prices down over time. Still, to provide my own anecdata, the main thing keeping me on a paid subscription is token costs for the more capable models.
Then it's not open-weight. Open-weight means "weights available under an open-source license", and licenses prohibiting commercial use are not open-source.
The nearest analogue would be "weights available", I guess.
Meta did this from the very beginning with the original Llama's. Open weights, but you have to use an account with them and agree to an EULA.
ya especially because it's huge, you aren't running this on your desktop
Well, this is much better than nothing -- we should be grateful that whatever the market conditions and corporate incentives, a major lab is releasing a capable open-weight model. But still very far behind DeepSeek and GLM. Even on consumer hardware, Qwen 3.8 27B is going to compare favorably to this.
How does this compare to Muse Spark 1.3 contribute? I've used Spark 1.3 a lot lately , since its currently free to use via OpenCode. I haven't had the best experience in the past with models from Meta. But Spark 1.3 is has been surprisingly great. Especially for tool calling.
Please give it a try and let us know ;) judging by the benchmark scores I would not expect too much of a difference.
Main difference seems to be that this one is cheaper/easier to host.
Ehm.. I have a model that runs on my older gaming desktop that scores as well (or better) as this model does on DeepSWE. This model does better than Flash Next in many areas and I am sure is much stronger overall, but hmm.
Muse Spark Open: 816B 42B
Zuck is the good guy here. Damn
It's really nice that all AI pages are using canvas animations. A few days ago, Gemma 4's animation wasn't that impressive. Same with Muse. So far, the best animations I've seen are from OpenAI and Anthropic.
from what i understand its somewhere between muse spark 1.3 and 1.2 in some coding benches.
US OPEN WEIGHT IS NOT DEAD THANKS ZUCK
Is Google not gonna make another Gemma or something?
Even if they stopped, the Chinese model release hit an all-time high this summer so I think we're good.
Nice a Mastodon shoutout.
i love seeing major ai labs release open-weight models. really hope we'll see more of this from openai and eventually anthropic too.
great, I've been reading series of papers related to open source/open weight LLM Backdoors.
It's 100% possible, curious when will be the first incident.
Having something built by entity with HQ in the wester world is always a bit safer. That could be mistral, but they decided to be a consultancy company, not an ai lab.
Right. The US has famously never installed government-mandated backdoors in security-critical technology like Dual_EC_DRBG.
Link to papers?
"Muse Spark Open was optimized to run on Mac Studio with M5 Ultra, and together they’re the best way to run a powerful model on your own desktop."
”Serve the model on either two Mac Studios, with M5 Ultra and 512GB of unified memory; or four Mac Studios with 256GB of unified memory, for a total of 1TB of unified memory, enough to hold a NVFP4 quantized version of Muse Spark Open”
Can not find any info on inference speed.
its around 20tps
Read it again. Two M5s.
Wait read that again. Two 512GB, unreleased model M5s.
This is gonna cost like 50k to run at home.
Closer to ~$30K if I had to guess. A 256 version is $11,299.00 (2TB SSD, 256GB RAM, 36-core CPU, 80‑core GPU), so yeah, 4 of those would be close to $50K but the 96->256 RAM upgrade is $4K, if we assume 512 will be double that it's ~$15K/ea.
But yes, still a massive amount of money and that's before we know the TPS or any other stats.
Why would anyone want to run an LLM on Apple's hardware? Why not buy an NVIDIA GPU?
okay that's gonna be interesting to try
can we stop these safety risk.... Feels like gpt 2/3 days, and now we know there wasn't any safety issue with gpt 2/3....