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.
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.
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.).
They’re not really. They make the things useful for two classes of users: Mac Studio for people running models locally and Siri backed by Gemini for voice interaction.
They’ll likely embed text models more deeply in the future, but the history of “Apple Intelligence” doesn’t give a lot of hope that they’ll nail it.
So they’re not selling shovels (a precursor to making the thing) but products and services to use it.
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.
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 playing 3d chess here -- they can just absolutely kill it in making the HW available for everyone to run models at home, and will do a better job than Nvidia especially when M7 comes out which is specifically designed for AI workloads.
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.)
The only question you need to ask is whether or not Apple will do anything to design and engineer memory in-house, the money, and the capability is there that is the challenge. because you don’t need to select $20,000 computer if you get that under control by bringing it in-house.
In short, Apple needs to roll up there sleeves and get busy the three hyenas of memory and that nameless memory company in China will not sell you memory when you need it at a reasonable price. In the past Motorola said no, IBM said no, and Intel said no. This is another one of those times for Apple to adapt by designing and engineering around the competition.
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.
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.
I think the iOS App Store is going to be in steep decline in a few years. People will generally have a few apps: ChatGPT, Instagram, messaging, and something like a Photos/Camera app.
Everything else will be done in your AI app.
I've been hooking up more and more data and plugins into ChatGPT/Claude. I only want to use it. I don't want to use other apps if I don't have to. For example, research, buy, plan. I prefer not to use other apps.
I think all it takes is for OpenAI to make a phone from the ground up for this and Apple is going to have serious competition.
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.
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.
Google, who is far more qualified, and is getting a measly 1 billion dollar per year refund on that $20 billion dollars they currently paying Apple for a default search position on Mac OS. Meta, whose software prowess is questionable, when compared to Google or Microsoft isn’t even in the game they are getting how should I put this Apples charity/tolerance which is all OpenAI and Google are currently getting.
One is getting a $1 billion refund from what they already pay Apple, and the other two are getting nothing from Apple.
Meta won’t be getting anything from Apple in the future, other than tolerance/charity.
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.
Most of the following tables show relative speedups; not tok/s numbers.
I agree no home AI users should really use bf16^; but I see two defensible justifications: (1) showing that their new hardware's speedups scale to full precision too [crucially; NVIDIA is focusing more and more on lower quants], and (2) trying to sell to the crowd running IQ3XXS or something on 16GB.
^q8 ~= bf16 is a good general rule of thumb. And friends don't let friends quantize KV cache.
Partner!? I don't see where you get that from the article, and Apple & Facebook have traditionally not been best of friends (see everything iOS implemented to limit Facebook's overreach).
I'm guessing they chose Muse because 1. It's not Chinese so it'll look good for Trump's AI cronies (Apple is in the administration's spotlight) and 2. It's fairly recent and thus on people's mind.
Eh, Apple spent years suing Samsung while also buying parts from them for their products. You can be a partner and an enemy at the same time at the high levels of the game.
I mean that they're doing joint marketing on this particular thing. Muse Spark marketing features Mac Studios quite prominently, and Apple's material spotlights Muse Spark.
For what it’s worth, Muse Glimmer is by far the most usable model on my 32GB M1 Max. It gets stuff done very competently while Qwen never gets done reasoning.
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.
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.
Tim Cook, smiled. with OpenAI and Google a smile is easy when it isn’t costing you anything on the Apple side so John can smile away in addition, the AI models are getting smaller and more powerful by the day something else to smile about if you’re Apple.
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.
To be clear, at 0.75 words per token, that's about ~6,100 words (roughly ~12 pages), which I suppose could be useful, but as other commenters have pointed out, you'd need some kind of orchestrated swarm/hive that could smartly fit payload to within that envelope
Agent swarms have figured out how to mostly work around context window limitations. There's no reason the same ideas can't be applied on a smaller scale, at least in principle.
> 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.
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.
They went with several open weight models. Presumably they did that because they wanted their research to be reproducible (once the 512 GB M5 Ultra actually hits the market). Using say a Gemini model they have access to only due to their partnership with Google would not be as useful for people to understand what Apple did.
Apple has too many arbitrary, pointless grudges for Ternus to take them all seriously.
Google partnership for Apple Intelligence, Meta partnership for local models, and allegedly NVLink Fusion support for M8 GPUs. Maybe the White House told him that it's time to start burying hatchets. I don't get the impression that Apple's prior AI strategy under Cook made any real progress.
Not pointless, all of that led to Apple Silicon, and you seem to be ignoring the fact that it’s not costing Apple any money. None of their hookups. This so-called cooperation is costing Apple a dime. But it is costing the other side billions of dollars.
Smile, the models are getting smaller and better, every generation of Apple Silicon is getting better in the opposite direction. The only problem is the cost of memory and that is within the design engineering ability in-house it is also within their financial capacity, so if I’m John or Tim, I too would wake up with a smile because I can fix the one thing that’s giving us trouble memory because we have the talent and the money to fix it in the next four years.
Am I right that investing in your own ML hardware only makes sense when you are dealing with private data and confidential sessions, right? (Not counting development or tinkering work).
If you think of it as API access vs temporary hosting running the same open model, it makes sense to run the hardware yourself whenever you are sure it can be fully utilized.
> 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!
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.
Exactly why does Apple need to do any model training/development? They are out here selling the shovels.
Apple’s Photos ecosystem is privacy oriented and as a result some (now) baseline features require the use of local models.
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.
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Exactly my thought as I read 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.
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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.
> They are out here selling the shovels
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.).
They’re not really. They make the things useful for two classes of users: Mac Studio for people running models locally and Siri backed by Gemini for voice interaction.
They’ll likely embed text models more deeply in the future, but the history of “Apple Intelligence” doesn’t give a lot of hope that they’ll nail it.
So they’re not selling shovels (a precursor to making the thing) but products and services to use it.
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.
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.
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Apple playing 3d chess here -- they can just absolutely kill it in making the HW available for everyone to run models at home, and will do a better job than Nvidia especially when M7 comes out which is specifically designed for AI workloads.
Both companies are profitable and AI is not their main line of business. This affects pure AI companies.
As open weight models continue to match frontier models, the race to $0 will continue to benefit supporters of running open weight models locally.
How big is the local LLM market? I have trouble imagining Apple will move enough units to make $20k/ea worth it for them as a business segment.
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.)
The only question you need to ask is whether or not Apple will do anything to design and engineer memory in-house, the money, and the capability is there that is the challenge. because you don’t need to select $20,000 computer if you get that under control by bringing it in-house.
In short, Apple needs to roll up there sleeves and get busy the three hyenas of memory and that nameless memory company in China will not sell you memory when you need it at a reasonable price. In the past Motorola said no, IBM said no, and Intel said no. This is another one of those times for Apple to adapt by designing and engineering around the competition.
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They have been sold out of a lot of units, due to people developing AI, so apparently, it is significant.
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.
Probably worth it just as an investment into their developer ecosystem. They could probably sell at a deep loss and still justify it.
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.
I disagree.
I think the iOS App Store is going to be in steep decline in a few years. People will generally have a few apps: ChatGPT, Instagram, messaging, and something like a Photos/Camera app.
Everything else will be done in your AI app.
I've been hooking up more and more data and plugins into ChatGPT/Claude. I only want to use it. I don't want to use other apps if I don't have to. For example, research, buy, plan. I prefer not to use other apps.
I think all it takes is for OpenAI to make a phone from the ground up for this and Apple is going to have serious competition.
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.
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How many 5080 16 Gig cards would you have to buy to run the same model, and what would that cost?
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.
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Now scale that up to Nvidia and you've understood the entire AI market
Still cheaper than the equivalent Nvidia GPU setup btw.
The NVIDIA GPU can be changed as new models become available, without throwing away the whole computer.
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I wonder how many units Meta bought to convince Apple to do a collaborative advertisement.
That is generally how hardware gets sold yes
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It won't be free for Apple to use Meta's model in the future.
Google, who is far more qualified, and is getting a measly 1 billion dollar per year refund on that $20 billion dollars they currently paying Apple for a default search position on Mac OS. Meta, whose software prowess is questionable, when compared to Google or Microsoft isn’t even in the game they are getting how should I put this Apples charity/tolerance which is all OpenAI and Google are currently getting.
One is getting a $1 billion refund from what they already pay Apple, and the other two are getting nothing from Apple.
Meta won’t be getting anything from Apple in the future, other than tolerance/charity.
Even in the unlikely case that does happen, there are plenty of other open weight models like Qwen, GLM, and Mistral to name a few
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.
They clearly list out the quants in Table 1.
Most of the following tables show relative speedups; not tok/s numbers.
I agree no home AI users should really use bf16^; but I see two defensible justifications: (1) showing that their new hardware's speedups scale to full precision too [crucially; NVIDIA is focusing more and more on lower quants], and (2) trying to sell to the crowd running IQ3XXS or something on 16GB.
^q8 ~= bf16 is a good general rule of thumb. And friends don't let friends quantize KV cache.
> cross-brand marketing for Apple's partner, Meta
Partner!? I don't see where you get that from the article, and Apple & Facebook have traditionally not been best of friends (see everything iOS implemented to limit Facebook's overreach).
I'm guessing they chose Muse because 1. It's not Chinese so it'll look good for Trump's AI cronies (Apple is in the administration's spotlight) and 2. It's fairly recent and thus on people's mind.
Eh, Apple spent years suing Samsung while also buying parts from them for their products. You can be a partner and an enemy at the same time at the high levels of the game.
I mean that they're doing joint marketing on this particular thing. Muse Spark marketing features Mac Studios quite prominently, and Apple's material spotlights Muse Spark.
For what it’s worth, Muse Glimmer is by far the most usable model on my 32GB M1 Max. It gets stuff done very competently while Qwen never gets done reasoning.
[dead]
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.
GLM 5.3 Flash on 128GB Ram would have to be 2-bit right?
I'm no expert but it seems the consensus is you get a suboptimal experience under 4-bit. Please correct me if I'm mistaken here
MoE, especially these Chinese ones trained on low-bits already is actually quite quantizable to lower bits, unlike smaller / dense models.
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Not exactly your average "desk", but still neat.
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.
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There was the M3 Ultra 512GB Mac Studio, though.
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Puts into perspective how cheap the frontier models are via subs or even API.
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Hilarious how they managed to put out an entire press release about Meta’s technology without mention Meta other than what is in the code references.
It’s not a press release and not about Meta’s technology.
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.
For the press, you had the release from meta here: https://research.meta.ai/blog/muse-spark-open
...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...
Tim Cook, smiled. with OpenAI and Google a smile is easy when it isn’t costing you anything on the Apple side so John can smile away in addition, the AI models are getting smaller and more powerful by the day something else to smile about if you’re Apple.
Still weird, and against brand, for them to lead with this particular model from that particular company. The groans are still earned.
They don’t lead with it. The title of the actual article is “Local and Distributed Large LLM Inference with MLX and RDMA on Mac Studio”.
The submitter changed it to “Apple ML Research: Muse Spark Open with MLX on Mac Studio”
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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.
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That first bar chart showing between 15-36 tokens/second is for a 8,192 token context. Thats not nearly enough context to be useful these days
To be clear, at 0.75 words per token, that's about ~6,100 words (roughly ~12 pages), which I suppose could be useful, but as other commenters have pointed out, you'd need some kind of orchestrated swarm/hive that could smartly fit payload to within that envelope
Agent swarms have figured out how to mostly work around context window limitations. There's no reason the same ideas can't be applied on a smaller scale, at least in principle.
Could you please paste some links about this that i can further read? Really curious
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> 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.
(Emphasis added.)
I run my toaster for hours on end too. Love my sandwiches.
Bad comparison, if I needed to make toast I'd use a Pentium 4 not an M5 Ultra
Once a month, I run my toaster oven the entire night to take advantage of off-peak energy rates.
Imagine if you had 4 of them!
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A 800B model that performs way worse than other 300B ones. Well...
What are you using to determine performance?
Most people are probably looking at: https://artificialanalysis.ai/models/comparisons?compare=mus...
This is still a welcome model, even if I won't be running it at home
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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
Very cool
Has anyone adapted the open Muse model to work like a decision model?
Sort of like Laya but based on muse spark
So Apple is the NVIDIA of open weight in this case
Apple is the AMD of open weight. No LLMs of their own, but a large appetite for partnerships.
I am super excited about this.
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.
Anyone know why they went with Meta models?
They went with several open weight models. Presumably they did that because they wanted their research to be reproducible (once the 512 GB M5 Ultra actually hits the market). Using say a Gemini model they have access to only due to their partnership with Google would not be as useful for people to understand what Apple did.
Apple has too many arbitrary, pointless grudges for Ternus to take them all seriously.
Google partnership for Apple Intelligence, Meta partnership for local models, and allegedly NVLink Fusion support for M8 GPUs. Maybe the White House told him that it's time to start burying hatchets. I don't get the impression that Apple's prior AI strategy under Cook made any real progress.
Not pointless, all of that led to Apple Silicon, and you seem to be ignoring the fact that it’s not costing Apple any money. None of their hookups. This so-called cooperation is costing Apple a dime. But it is costing the other side billions of dollars.
Smile, the models are getting smaller and better, every generation of Apple Silicon is getting better in the opposite direction. The only problem is the cost of memory and that is within the design engineering ability in-house it is also within their financial capacity, so if I’m John or Tim, I too would wake up with a smile because I can fix the one thing that’s giving us trouble memory because we have the talent and the money to fix it in the next four years.
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That makes sense I also assume there’s better allyship with Meta than X long term geopolitically and they are already in bed with google on search
openai and anthropic are too chaotic to bet on would be my guess
Am I right that investing in your own ML hardware only makes sense when you are dealing with private data and confidential sessions, right? (Not counting development or tinkering work).
If you think of it as API access vs temporary hosting running the same open model, it makes sense to run the hardware yourself whenever you are sure it can be fully utilized.
Many thought Apple should be competing with the frontier labs while they see themselves as competing with Nvidia.
Right now, they're competing with neither.
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Preparing to upsell those very expensive Macs coming later this month, are we.
I -- love -- an article -- full of -- em -- dashes.
If you type the following into Microsoft Word:
> 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.
Do note that those are en-dashes and not em-dashes.
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!
Em dashes—love ’em!—but they do. not. need. personal. space.