Garageband DAW + MacOS 14.4 Roland Juno-D7 synthsizer, for 8-bit audio complementary compact disk format as AIFF, WAV, or MIDI appliance, in which under SLA-royalties licenses, binary 44.1 Khz sample rate sets the reproducer for reference level.
macOS 26.2 enables fast AI clusters with RDMA over Thunderbolt
221–230 of 304 posts
Re: macOS 26.2 enables fast AI clusters with RDMA over Thunderbolt
#222I am waiting for M5 studio but due to current price of hardware I'm not sure it will be at a level that I would call affordable. Currently I'm watching for news and if there is any announcement prices will go up I'll probably settle for an M4 Max.
Re: macOS 26.2 enables fast AI clusters with RDMA over Thunderbolt
#223Earlier quoted context omitted.
Every time I'm tempted to get one of these beefy mac studios, I just calculate how much inference I can buy for that amount and it's never a good deal.
Are the inference providers profitable yet? Might be nice to be ready for the day when we see the real price of their services.
Isn't it then even better to enjoy cheap inference thanks to techbro philanthropy while it lasts? You can always buy the hardware once the free money runs out.
Re: macOS 26.2 enables fast AI clusters with RDMA over Thunderbolt
#224Earlier quoted context omitted.
You can get a Mac Mini for $600 with 16GB of RAM and it will be more powerful than the "PC in every home" people would need for any common software. The personal computing situation is great right now. RAM is temporarily more expensive, but it's definitely not ending any eras.
Not Apple’s ram.
RAM prices have exploded enough that Apple's RAM is now no longer a bad deal. At least until their next price hikes.
We're going back to the "consumer PCs have 8GB of RAM era" thanks to the AI bubble.
Re: macOS 26.2 enables fast AI clusters with RDMA over Thunderbolt
#225Earlier quoted context omitted.
It’s just depressing that the “PC in every home” era is being rapidly pulled out from under our feet by all these supply shocks.
Huh? Home PCs are as cheap as they’ve ever been. Adjusted for inflation the same can be said about “home use” Macs. The list price of an entry level MacBook Air has been pretty much the same for more than a decade. Adjust for inflation, and you get a MacBook air for less than half the real cost of the launch model that is massively better in every way. A blip in high end RAM prices has no bearing on affordable home c…
> I can get a Ryzen 7 system with 32gb of ddr5, and a 1tb drive delivered to my house before dinner tomorrow for $500 + tax
That's an amazing price, but I'd like to see where you're getting it. 32GB of RAM alone costs €450 here (€250 if you're willing to trust Amazon's February 2026 delivery dates).
Getting a PC isn't that expensive, but after the blockchain hype and then the AI hype, prices have yet to come down. All estimations I've seen will have RAM prices increase further until the summer of next year, and the first dents in pricing coming the year after at the very earliest.
Re: macOS 26.2 enables fast AI clusters with RDMA over Thunderbolt
#226dang I wish I could share md tables. Here’s a text edition: For $50k the inference hardware market forces a trade-off between capacity and throughput: * Apple M3 Ultra Cluster ($50k): Maximizes capacity (3TB). It is the only option in this price class capable of running 3T+ parameter models (e.g., Kimi k2), albeit at low speeds (~15 t/s). * NVIDIA RTX 6000 Workstation ($50k): Maximizes throughput (>80 t/s). It is sup…
You can keep scaling down! I spent $2k on an old dual-socket xeon workstation with 768GB of RAM - I can run Deepseek-R1 at ~1-2 tokens/sec.
I did the same, then put in 14 3090's. It's a little bit power hungry but fairly impressive performance wise. The hardest parts are power distribution and riser cards but I found good solutions for both.
Re: macOS 26.2 enables fast AI clusters with RDMA over Thunderbolt
#227Earlier quoted context omitted.
It’s been terrible for years/forever. Even Xserves didn’t really meet the needs of a professional data centre. And it’s got worse as a server OS because it’s not a core focus. Don’t understand why anyone tries to bother - apart from this MLX use case or as a ProRes render farm.
iOS build runner. Good luck developing cross-platform apps without a Mac!
Practically, just run the macos-inside-kvm-inside-docker command. Not very fast, but you can compile the entire thing outside of the VM, all you need is the final incantations to get Apple's signatures on there.
Legally, you probably need a Mac. Or rent access to one, that's probably cheaper.
Re: macOS 26.2 enables fast AI clusters with RDMA over Thunderbolt
#228Earlier quoted context omitted.
I wonder what the actual lifetime amortized cost will be.
Every time I'm tempted to get one of these beefy mac studios, I just calculate how much inference I can buy for that amount and it's never a good deal.
Nevermind the fact that there are a lot of high quality (the highest quality?) models that are not released as open source.
Re: macOS 26.2 enables fast AI clusters with RDMA over Thunderbolt
#229Earlier quoted context omitted.
Every time I'm tempted to get one of these beefy mac studios, I just calculate how much inference I can buy for that amount and it's never a good deal.
Every time someone brings up that, it brings me back memories of trying to frantically finish stuff as quickly as possible as either my quota slowly go down with each API request, or the pay-as-you-go bill is increasing 0.1% for each request. Nowadays I fire off async jobs that involve 1000s of requests, billion of tokens, yet it costs basically the same as if I didn't. Maybe it takes a different type of person, than…
But the downside is that you are stuck with inferior LLMs. None of the best models have open weights: Gemini 3.5, Claude Sonnet/Opus 4.5, ChatGPT 5.2. The best model with open weights performs an order of magniture worse than those.
Re: macOS 26.2 enables fast AI clusters with RDMA over Thunderbolt
#230Earlier quoted context omitted.
Every time someone brings up that, it brings me back memories of trying to frantically finish stuff as quickly as possible as either my quota slowly go down with each API request, or the pay-as-you-go bill is increasing 0.1% for each request. Nowadays I fire off async jobs that involve 1000s of requests, billion of tokens, yet it costs basically the same as if I didn't. Maybe it takes a different type of person, than…
But the downside is that you are stuck with inferior LLMs. None of the best models have open weights: Gemini 3.5, Claude Sonnet/Opus 4.5, ChatGPT 5.2. The best model with open weights performs an order of magniture worse than those.
The best weights are the weights you can train yourself for specific use cases. As long as you have the data and the infrastructure to train/fine-tune your own small models, you'll get drastically better results.
And just because you're mostly using local models doesn't mean you can't use API hosted models in specific contexts. Of course, then the same dread sets in, but if you can do 90% of the tokens with local models and 10% with pay-per-usage API hosted models, you get the best of both worlds.