So, Meta will spend millions/billions on building open-weight models and then people will send Apple $20,000 to run them. Seems like a good deal for Apple.
No. Apple’s Photos ecosystem uploads all Photos effectively unencrypted to Apple in a way that is readable by Apple, allowing Apple to do server-side analysis of all content. This content is also provided to US or Chinese federal authorities without due process or warrants.
I've found some strange guardrails with the Foundation models. Like blocking describing of any photo with a single woman in it. It does this relatively consistently but with a different subset every time. Like out of 10 photos in the set, it's skips 6 then the next run, it skips 3. Have you seen guardrail issues like this?
I am going to assume that means "a women by herself" and not that it can magically detect her marital status before refusing to continue (I suppose it could be laser-focused on wedding rings...)
I don't want any of these features and yet they've been forced upon me and are now getting in the way of me actually using my computer how I want. Games and heavy applications slow down and stutter because of "AI" background processes, even with App Tamer, my machine runs too hot to even touch when just idling. Compared to when I got my M2 in '23 and thought, "This is the fastest and snappiest machine I've ever used", my experience with an M5 and the latest macOS version is nothing short of an anti-consumer hellscape.
It's not just AI processes. But things like mediaanalysisd, duetexpertd, etc. even the freaking menu bar agent, all regularly pound my computer both when idling and when in use.
I recently installed App Tamer which is helping a bit, but it doesn't allow me to tame everything. I just checked now and duetexpertd was trying to use nearly two full cores. I have all iCloud integrations turned off, I do not use iCloud at all, and I do not have an iPhone, so I am flabbergasted that this iCloud desktop-phone syncing process runs at such high core usage in the background with extreme regularity. Proof: https://imgur.com/VkBh9bi
Unfortunately, this does nothing for services such as WindowServer which now often takes up 50-100% of a core just on its own.
I have reinstalled and upgraded to the latest version. This MacBook is only a few months old.
While typing this message and literally just having open Codex with one running task that is not doing anything substantial, and Firefox, with no high CPU usage from any tab, my laptop got so hot that I had to use another installed program, Stats, to manually bump up the fan since macOS also has absolutely garbage native system fan controls.
I have fibromyalgia and seem to be sensitive to hot keys; my Thinkpad regularly getting to hot to touch with even light usage is EXACTLY why I went and bought a MacBook one morning in '23. One morning I touched my keyboard and realized I was tired of singeing the nerves on my fingertips and having them hurt for hours. So now in '26, finding myself in the same exact position but with a much more expensive computer, I feel like I am living in a corporate hellscape nightmare.
One thing I have noticed is all of my cores can sit idle except a single one and that will still heat up the machine a lot. If the hardware is designed as such, the software MUST be designed to work around this and not blast a core for 10 minutes straight every time one of a dozen or two misbehaving system processes wants to complete a background task. If the software is not designed for this, the hardware is incomplete and faulty. This would be true if it was shipped as firmware; software doesn't get a free pass here.
But now Pages and Numbers recommend document titles for me, so.... worth it?
Interesting, this has not been my experience at all. I have an M5 machine and I'm not even aware of what AI features have been added, and it's streets ahead of my old M1 Pro in speed.
peep the comment I just made to the adjacent commenter, that has been my experience. I basically have to run my fans at full speed all day while working now in order to type on my computer (when I'm not using my magic keyboard). It is like a huge problem, not even just a small one but like something that is affecting me negatively every single day, increasingly making me want to break this machine in half and disappear into the woods.
Well, the moat is that cpu/npu development is very hard, requires talented engineers, and requires access to cutting edge fab process, all of which in turn costs a lot of $$$. So, sure, “anyone” who has all that can definitely breach Apple’s moat.
Apples largest competitive advantage has always been and always will be - trust combined with epic marketing to make the vast majority of people aware of it.
Trust of quality, privacy w.r.t data etc.
I’m sorry but I’ve seen so many bad takes re. Apple over the years. Many of you think you are so smart on topics outside of your domain - but you’re not.
What exactly about the parent comment is wrong? Apple has always been good at making hardware that people are willing to pay a premium to use. I can easily imagine them playing a similar role in local LLMs to the role they played in personal computing in the 1980s or smartphones in the 2010s.
just wish their containerization / linux compat wasn't so nerfed. Just moved my homelab's inference engine over to a GB10 from a Mac MTP setup to have first class k8s support, as well as open up access to the broader CUDA ecosystem (cuOpt etc.).
Nvidia is also selling the shovle but they train their own LLM and by doing so they can fine tune their CUDA/Chip to have a better shovel than others.
Nvidia's own model does not have to beat OpenAI or any SOTA model. Nvidia just need to build up the in house expert and knowledge to make a better chip since hyperscalers and none hyperscaler like Meta is building their own AI chip now.
Apple can chose not to train their own model to learn how to sell a better shovel.
That's just BS cope. They tried and miserably failed - and still are failing badly. Remember, they were the ones to push for AI with "Siri". Many firings later and 2 CEOs later we are still exactly in square one. The fucking thing can't even get who I want to call, let alone set an alarm without touching my phone FFS.
apple is pretty well set up for this because they do all their hardware in house. if they wanted to, they could pivot in this direction. Ternus is also a hardware guy.
I think the bet is that it will get bigger, both at the consumer level, and with orgs eventually discovering ready-made solutions for having their own on-prem inference.
100,000 units a year would be $2 billion annually.
And usually, most of a company's revenue comes from the heaviest spenders.
So figure there are some out there who will by tens of these because they have some "idea to change the world". (Whether true or not.) Some startups, etc.
I'd wager, across planet earth right now, it should be trivial to push 100,000 units per year. Probably closer to 500,000 units per year. So that's an easy $10 billion a year.
$2-10 billion is meaningful for any company. Even one as large is Apple.
Especially if it's a first step towards a stronger foothold in the AI inference space.
They can then expand down market to grow the pie further.
For example, with truly dedicated inference boxes.
And given the demand for $7,000 Sparks and sold out $5,000 GPUs, the market for these $20,000 machines is likely much higher than my 100,000 unit lower bound. (Earth is a big place. It doesn't take much excitement to generate massive sales in a space with currently insatiable demand.)
idk but naive me sees the article say the model needed 158GB of RAM so I guess 10x 16GB GPUs? Apparently retail price is $1k per 5080 16GB but they're actually selling at $1.5k so I guess $15k for just the GPUs.
158GB number is for Mistral Medium 3.5 128B model, Muse Spark is way larger.
Muse Spark Open would require more than 1.6TB of RAM in its native precision (BF16) for the model parameters alone. In a 4-bit quantized (NVFP4) format, the model memory footprint is lowered to about 493GB.
In any case, I'd much rather run GLM 5.3 Flash on that sort of hardware. Better fit for the performance profile, and will probably beat Muse Spark Open in most uses.
They have the best strategy of bigtechco: let others figure out the squishier layers while they focus on selling the best hardware. Privacy has incredible value to a lot of individuals and industries. $20-80k in hardware that *appreciates* the moment you buy it is a reasonable investment for a lot of SMBs, and still less than a single Nvidia rig. Privacy pays.
If we're being frank here, Apple's AI strategy lags behind Nvidia, AMD and even Google. Nvidia has CUDA, AMD ships CDNA, Google has the TPU family, and Apple... well, Apple discontinued the rackmount Mac Pro, forfeit the HPC ARM datacenter to Nvidia's Grace CPUs, refuses to sign CUDA drivers, and spent half a decade designing raster GPUs instead of GPGPUs.
All of their competition has real inference and training stacks. Apple ships an iGPU and a smartphone NPU, they're reliant on Nvidia and Google hardware to deliver Apple Intelligence.
Saying they are lagging assumes they are in the same race
They aren’t
Apple is a high net worth consumer technology integrator that focuses on highest coherence user experience. They pick the most efficient stable technology, integrate it and sell the package
Apple only needs to lead in precisely that they don’t need to lead in literally anything else
in fact if they tried to lead in those other things they would give up their integration capacity
I think they're sandbagging Qwen pretty heavily by benchmarking Meta's model at 4-bit, against Qwen only at native BF16 (and nothing else shown). That's not a quant most users would choose.
I get that this is cross-brand marketing for Apple's partner, Meta; but really Muse Spark isn't even in the top *ten* models I'd consider if I had a 512 GB M5 in my paws. Or a cluster of them.
FWIW, single M5 Ultra with 256GiB runs DeepSeek 4.1 Flash 0910 just fine (~3200 tok/s prefill, ~45 tok/s). M5 Max w/ 128GiB runs GLM 5.3 Flash also fine.
I also benchmarked both on quality benchmarks (MathArena Apex / GPQA Diamond etc) to make sure the quality is what you get from the official numbers.
> peak power consumption is about 200W for the Mac Studio with M3 Ultra, and 300W for the Mac Studio with M5 Ultra — a fraction of the power used by a common toaster.
I find it bizarre of Apple to do anything publicly in partnership with Meta. I always thought the reasons Apple snubbed Facebook were out of values and principles not business. That could be wrong. Now that Meta has a line of business that Apple can finally charge a platform tax on there is new reason to start sleeping with them.
> I always thought the reasons Apple snubbed Facebook were out of values and principles not business.
Apple is showing a masterclass of how to tow the line perfectly. Or more candily "have the cake and eat it too".
If you ask anyone, what's one company that'll stand up to government requests, most reasonable people would say Apple. Yet, Apple is extremely strategic when it comes to China. It thrives in that country, without being associated with CCP.
Same here - they are happy to partner with Meta, without getting any of the bad "aura".
To be clear, all companies do this just that Apple alone has a successful strategy here. Tim Cook personally gifted the current adminstration with a gold plaque, something none of the other "dirty" CEOs did. Such a public gesture, yet nobody would think that Apple is in bed with the current administration. Its marvelous to see this happen in real time.
I worked at a company where one of the pieces of hardware we were building at the time used a PowerPC CPU (this is back in ~2007). We had a rack of Apple xServe for build and test. The xServe was nice pieces of hardware compared to the x86 kit we had, more like Sun gear in their attention to detail and ease of use.
At my current company we compile for x86 and devs can do a full build and test environment on their laptops (which are 95% MacBook Pros) but it is unclear what will happen when Rosetta support is pulled. Now the interesting thing is we plan to move our stuff over to target the AWS Graviton (and other cloud ARM cores) as those instances are cheaper for generally the same performance (for what was are doing anyway) so the point will be moot. There will be some crossover point where having build and test servers in a rack will be cheaper than doing it in the cloud. At that point having Apple ARM based servers would be a win assuming we can build a binary that works for Graviton, et.al.
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 (see Figure 1), well above average human reading speed. 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.
Yes, I'm very glad they're doing this, but as of 2026, it's a bit academic. Pricing for the 512GB Mac Studio hasn't even been announced yet, but the 256GB version starts at $9499. So when you start talking about four of those puppies, it's substantial.
Someone else will do the math on how many years it would take to spend $40k on Muse Spark Open via OpenRouter.
But there are definitely people out there that will need to run these models locally (contractual or legal requirements). By validating a Mac Studio as an option for running local models, it might be the more appealing option.
For most of us though, you're right -- the OpenRouter option is far more cost effective.
It seems Apple figured they could sell more Mac Studios on the basis of frontier local AI models, profit to be made while users haven't figured out the math yet.
Don't get me wrong; in the long term I see the price of local inference falling as their chips continue to get 15-20% faster every year, and meanwhile the "intelligence per billion parameter" chart is rising. There will be a magic point where they cross, and "good enough" reaches your phone. That's when Apple starts to drink Nvidia's milkshake.
It’s not about the price of Apple products falling—I was commenting that the price of inference will continue to fall (chips getting faster). If “good enough” inference is in your pocket, this benefits Apple and harms Nvidia.
You can't really compare API prices though. The cards that they are running frontier models on in the datacenter can service hundreds of simultaneous requests and are at nearly 100% utilization. This thing would sit on your desk and be used to service a single user for a couple hours per day.
The main subsidy, from OpenAI, is ending 10/26. It’s still “cheap” for now compared to all the other non-open weight frontier models, but it won’t last since OpenAI’s stated goal is to make it so that there’s no much of a difference price wise between on demand API calls and subscriptions
It was only like $14k new. Why are you people acting like nobody has this stuff? People were already building clusters when that thing first came out. The research isn't bunk just because the refresh hasn't landed yet, my god.
Maybe you (or i?) missed the point - it wasn't that nobody has those machines, it was that at $40k for 4x the 256GB versions is a substantial price, and so the parent parent was suggesting that it would be compared against OpenRouter. Ie how long would it take someone to spend $40k worth of credits on OpenRouter?
I've had the same thought on self hosting for the past 2 years. Eg i could invest $10k into hardware or spend those on credits. I chose credits, because by the time i spend 10k i suspect the market will have dramatically shifted in requirements, compute, etc.
> I chose credits, because by the time i spend 10k i suspect the market will have dramatically shifted in requirements, compute, etc.
I agree, at least for inference credits. But anecdotal counterpoint: about ten years ago I put together a system using about $1600 worth of new and used components. Including electricity that TCO has been something like $14/month. I'd be hard pressed to find 128GB of RAM, 12 cores of x86_84, and 40 TiB (raw) worth of disk, and unlimited ingress/egress traffic for rent at that rate. That's what, $0.02 an hour, rounding up?
Yes, a hyperscaler's rent-a-computer would get faster and be more energy efficient. Possibly more carbon efficient over it's lifetime, although I have no way to quantify that. But in raw dollars it would have costed me absurdly more to rent. On the other hand, my machine has sat cold and unused for multiple months when I didn't need it to be running, and I don't consume all of it's resources around the clock. So maybe I could have gotten away with a lesser amount of local compute in a Raspberry Pi or similar, and an equally small cloud box for $5 a month or something. I dunno. I don't even own a laptop or desktop, and try to use 'cloud' services whenever practical. (Edit: always giving priority to privacy in those circumstances; only data I don't care about goes up.)
There are tradeoffs. I don't use my local server for LLM inference, it's far to inefficient for that; so I'll happily pay OpenRouter per token as needed. So I do think that hosted compute for rent does have a purpose. I guess all that's to say that I don't see it in black and white; there are times to invest in local hw and times to ride on the optimized bleeding edge of what other companies are willing to host in big datacenters.
This turned into a bit more of a ramble than I intended.
Haven't smallish (16-32 GB VRAM) open-weights models been getting better and more capable over the last few years? Seems like what you can run on 'a laptop' today beats the pants off of whatever you could run on that same laptop two years ago.
I guess. I found that Gemini 3.1 Pro and 4 Argon are by far the best at what I like to use LLMs for. Even 3.8 Flash doesn't compare, and I'd imagine something I could run on my local hardware (M4 Max 128GB) would be just as disappointing as the rest, at least for now, even if better than earlier models. One of the reasons I've been hoping to see diffusion language models eventually.
It's a good article which showcases direction of travel, i am sure mac studio will get more and more powerful in coming generations but for now it's way to expensive for local setup.
yeah especially because it's not very easy to configure everything sensibly for a multi-use case setup, they really need better mac containers on the mac itself
While right now Mac Studio is not a budget device and it certainly costs more than the subsidised subscription, the promise that one day we can run these models on commodity hardware is exciting.
> In this post, we will 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.
You will get
> In this post, we will 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.
Not everything with em dashes is the product of generative AI... some of it is the word processor correctly following the rules of grammar and punctuation.
If used correctly, I find the em dash one of the most elegant and reader-friendly ways to structure certain parts of a text — that it got such a bad reputation lately really is a shame!
The Foundation models keep a surprisingly decent set of data available for fast filtering by a local LLM.
I’ve found a slower, second pass, is needed by Qwen to clean it up. (I am running on the new Mac Mini m5 pro 64gb.)
I’ve run my own tests using a date range, a short description of a red cedar planter box build I did last year, and a set of known correct photos.
There is some cool stuff you can do without sending your family’s pics up to any of the frontier model providers.
Nothing about iCloud Photos is private.
I switched to Gemma 4 instead.
I recently installed App Tamer which is helping a bit, but it doesn't allow me to tame everything. I just checked now and duetexpertd was trying to use nearly two full cores. I have all iCloud integrations turned off, I do not use iCloud at all, and I do not have an iPhone, so I am flabbergasted that this iCloud desktop-phone syncing process runs at such high core usage in the background with extreme regularity. Proof: https://imgur.com/VkBh9bi
Unfortunately, this does nothing for services such as WindowServer which now often takes up 50-100% of a core just on its own.
I have reinstalled and upgraded to the latest version. This MacBook is only a few months old.
While typing this message and literally just having open Codex with one running task that is not doing anything substantial, and Firefox, with no high CPU usage from any tab, my laptop got so hot that I had to use another installed program, Stats, to manually bump up the fan since macOS also has absolutely garbage native system fan controls.
I have fibromyalgia and seem to be sensitive to hot keys; my Thinkpad regularly getting to hot to touch with even light usage is EXACTLY why I went and bought a MacBook one morning in '23. One morning I touched my keyboard and realized I was tired of singeing the nerves on my fingertips and having them hurt for hours. So now in '26, finding myself in the same exact position but with a much more expensive computer, I feel like I am living in a corporate hellscape nightmare.
One thing I have noticed is all of my cores can sit idle except a single one and that will still heat up the machine a lot. If the hardware is designed as such, the software MUST be designed to work around this and not blast a core for 10 minutes straight every time one of a dozen or two misbehaving system processes wants to complete a background task. If the software is not designed for this, the hardware is incomplete and faulty. This would be true if it was shipped as firmware; software doesn't get a free pass here.
But now Pages and Numbers recommend document titles for me, so.... worth it?
They make and sell the hardware that will actually make AI something everyone can used without needing to rely on OpenAI/Anthropic et al.
Apple has never had a moat, anyone else could have built this sort of hardware but they haven't.
So they don't care which model wins, because it will always move out to the edge of the network, and they own a large chunk of that edge.
Hint: money is not the only reason.
Jeez.
Apples largest competitive advantage has always been and always will be - trust combined with epic marketing to make the vast majority of people aware of it.
Trust of quality, privacy w.r.t data etc.
I’m sorry but I’ve seen so many bad takes re. Apple over the years. Many of you think you are so smart on topics outside of your domain - but you’re not.
Lmao
> Many of you think you are so smart on topics outside of your domain
Turn the mirror around
just wish their containerization / linux compat wasn't so nerfed. Just moved my homelab's inference engine over to a GB10 from a Mac MTP setup to have first class k8s support, as well as open up access to the broader CUDA ecosystem (cuOpt etc.).
Nvidia's own model does not have to beat OpenAI or any SOTA model. Nvidia just need to build up the in house expert and knowledge to make a better chip since hyperscalers and none hyperscaler like Meta is building their own AI chip now.
Apple can chose not to train their own model to learn how to sell a better shovel.
And usually, most of a company's revenue comes from the heaviest spenders.
So figure there are some out there who will by tens of these because they have some "idea to change the world". (Whether true or not.) Some startups, etc.
I'd wager, across planet earth right now, it should be trivial to push 100,000 units per year. Probably closer to 500,000 units per year. So that's an easy $10 billion a year.
$2-10 billion is meaningful for any company. Even one as large is Apple.
Especially if it's a first step towards a stronger foothold in the AI inference space.
They can then expand down market to grow the pie further.
For example, with truly dedicated inference boxes.
And given the demand for $7,000 Sparks and sold out $5,000 GPUs, the market for these $20,000 machines is likely much higher than my 100,000 unit lower bound. (Earth is a big place. It doesn't take much excitement to generate massive sales in a space with currently insatiable demand.)
In any case, I'd much rather run GLM 5.3 Flash on that sort of hardware. Better fit for the performance profile, and will probably beat Muse Spark Open in most uses.
All of their competition has real inference and training stacks. Apple ships an iGPU and a smartphone NPU, they're reliant on Nvidia and Google hardware to deliver Apple Intelligence.
They aren’t
Apple is a high net worth consumer technology integrator that focuses on highest coherence user experience. They pick the most efficient stable technology, integrate it and sell the package
Apple only needs to lead in precisely that they don’t need to lead in literally anything else
in fact if they tried to lead in those other things they would give up their integration capacity
As open weight models continue to match frontier models, the race to $0 will continue to benefit supporters of running open weight models locally.
I get that this is cross-brand marketing for Apple's partner, Meta; but really Muse Spark isn't even in the top *ten* models I'd consider if I had a 512 GB M5 in my paws. Or a cluster of them.
I also benchmarked both on quality benchmarks (MathArena Apex / GPQA Diamond etc) to make sure the quality is what you get from the official numbers.
(Emphasis added.)
The title (“Local and Distributed Large LLM Inference with MLX and RDMA on Mac Studio”) doesn’t even mention Muse.
It’s more like an advertorial for their tech.
Apple is showing a masterclass of how to tow the line perfectly. Or more candily "have the cake and eat it too".
If you ask anyone, what's one company that'll stand up to government requests, most reasonable people would say Apple. Yet, Apple is extremely strategic when it comes to China. It thrives in that country, without being associated with CCP.
Same here - they are happy to partner with Meta, without getting any of the bad "aura".
To be clear, all companies do this just that Apple alone has a successful strategy here. Tim Cook personally gifted the current adminstration with a gold plaque, something none of the other "dirty" CEOs did. Such a public gesture, yet nobody would think that Apple is in bed with the current administration. Its marvelous to see this happen in real time.
The datacenters are not full of Mac studios but Nvidia hardware.
They are known for macbooks.
And they are not willing to build proper apple servers for all the build systems people have.
At my current company we compile for x86 and devs can do a full build and test environment on their laptops (which are 95% MacBook Pros) but it is unclear what will happen when Rosetta support is pulled. Now the interesting thing is we plan to move our stuff over to target the AWS Graviton (and other cloud ARM cores) as those instances are cheaper for generally the same performance (for what was are doing anyway) so the point will be moot. There will be some crossover point where having build and test servers in a rack will be cheaper than doing it in the cloud. At that point having Apple ARM based servers would be a win assuming we can build a binary that works for Graviton, et.al.
The submitter changed it to “Apple ML Research: Muse Spark Open with MLX on Mac Studio”
...and Zuck and Ternus high-fiving each other here: https://x.com/finkd/status/2110778025629347999
"We're excited for mommy and daddy to be playing nice again," Meta AI chief Alexandr Wang told Axios here: https://www.axios.com/2026/10/15/apple-and-meta-bury-the-hat...
Someone else will do the math on how many years it would take to spend $40k on Muse Spark Open via OpenRouter.
For most of us though, you're right -- the OpenRouter option is far more cost effective.
Their competitors will. Anyone locked into these two ecosystems are willingly going along with it.
I've had the same thought on self hosting for the past 2 years. Eg i could invest $10k into hardware or spend those on credits. I chose credits, because by the time i spend 10k i suspect the market will have dramatically shifted in requirements, compute, etc.
I agree, at least for inference credits. But anecdotal counterpoint: about ten years ago I put together a system using about $1600 worth of new and used components. Including electricity that TCO has been something like $14/month. I'd be hard pressed to find 128GB of RAM, 12 cores of x86_84, and 40 TiB (raw) worth of disk, and unlimited ingress/egress traffic for rent at that rate. That's what, $0.02 an hour, rounding up?
Yes, a hyperscaler's rent-a-computer would get faster and be more energy efficient. Possibly more carbon efficient over it's lifetime, although I have no way to quantify that. But in raw dollars it would have costed me absurdly more to rent. On the other hand, my machine has sat cold and unused for multiple months when I didn't need it to be running, and I don't consume all of it's resources around the clock. So maybe I could have gotten away with a lesser amount of local compute in a Raspberry Pi or similar, and an equally small cloud box for $5 a month or something. I dunno. I don't even own a laptop or desktop, and try to use 'cloud' services whenever practical. (Edit: always giving priority to privacy in those circumstances; only data I don't care about goes up.)
There are tradeoffs. I don't use my local server for LLM inference, it's far to inefficient for that; so I'll happily pay OpenRouter per token as needed. So I do think that hosted compute for rent does have a purpose. I guess all that's to say that I don't see it in black and white; there are times to invest in local hw and times to ride on the optimized bleeding edge of what other companies are willing to host in big datacenters.
This turned into a bit more of a ramble than I intended.
I just also think that plenty of people buy clusters of Mac Studios anyway.
Has anyone adapted the open Muse model to work like a decision model?
Sort of like Laya but based on muse spark
This is still a welcome model, even if I won't be running it at home
While right now Mac Studio is not a budget device and it certainly costs more than the subsidised subscription, the promise that one day we can run these models on commodity hardware is exciting.
> In this post, we will 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.
You will get
> In this post, we will 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.
Not everything with em dashes is the product of generative AI... some of it is the word processor correctly following the rules of grammar and punctuation.