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Apple M3 Ultra

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Re: Apple M3 Ultra

#691

Earlier quoted context omitted.

In the US, normal circuits aren't always 20A, especially in residential buildings, where they are more commonly 15A in bedrooms and offices. https://en.wikipedia.org/wiki/NEMA_connector

While technically true, the NEMA 5-15R receptacles are rated for use on 20A circuits, and circuits for receptacles are almost always 20A circuits, in modern construction at least. Older builds may not be, of course. That said, if your load is going to be a continuous load drawing 80% of the rated amperage, it really should be a NEMA 5-20 plug and receptacle, the one where one of the prongs is horizontal instead of ve…

> and circuits for receptacles are almost always 20A circuits, in modern construction at least.

This is not true. Standard builds (a majority) still use 15-amp circuits where 20-amp is not required by NEC.

Re: Apple M3 Ultra

#692

Earlier quoted context omitted.

Maybe .1% of tasks need this RAM, why are they charging so much?

I don't need 512GB of RAM but the moment I do I'm certain I'll have bigger things to worry about than a $10K price tag.

This is Pascal's wager written in terms of ... RAM. The original didn't make sense and neither does this iteration.

Re: Apple M3 Ultra

#693
post #222

512GB of unified memory is truly breaking new ground. I was wondering when Apple would overcome memory constraints, and now we're seeing a half-terabyte level of unified memory. This is incredibly practical for running large AI models locally ("600 billion parameters"), and Apple's approach of integrating this much efficient memory on a single chip is fascinating compared to NVIDIA's solutions. I'm curious about how…

"unified memory" funny that people think this is so new, when CRAY had Global Heap eons ago...

The real hardware needed for artificial intelligence wasn't NVIDIA, it was a CRAY XMP from 1982 all along

Re: Apple M3 Ultra

#694
post #222

512GB of unified memory is truly breaking new ground. I was wondering when Apple would overcome memory constraints, and now we're seeing a half-terabyte level of unified memory. This is incredibly practical for running large AI models locally ("600 billion parameters"), and Apple's approach of integrating this much efficient memory on a single chip is fascinating compared to NVIDIA's solutions. I'm curious about how…

"unified memory" funny that people think this is so new, when CRAY had Global Heap eons ago...

It's new for mainstream PCs to have it.

Re: Apple M3 Ultra

#695

Earlier quoted context omitted.

I also wondered about binning, so I pulled together how heavily Apple's Max chips were binned in shipping configurations. M1 Max - 24 to 32 GPU cores M2 Max - 30 to 38 GPU cores M3 Max - 30 to 40 GPU cores M4 Max - 32 to 40 GPU cores I also looked up the announcement dates for the Max and the Ultra variant in each generation. M1 Max - October 18, 2021 M1 Ultra - March 8, 2022 M2 Max - January 17, 2023 M2 Ultra - June…

I’m missing the point. What is it you’re concluding from these dates?

I was referring to the additional year of delay between the M3 Max and M3 Ultra announcements when compared to the M1 and M2 generations.

The theory that the M3 Ultra was being produced, but diverted for internal use makes as much sense as any theory I've seen.

It makes at least as much sense as the "TSMC had difficulty producing enough defect free M3 Max chips" theory.

Re: Apple M3 Ultra

#696

Earlier quoted context omitted.

Why did it take so long for us to get here?

Some possible groups of reasons: 1. Until recently RAM amount was something the end user liked to configure, so little market demand. 2. Technically, building such a large system on a chip or collection of chiplets was not possible. 3. RAM speed wasn't a bottleneck for most tasks, it was IO or CPU. LLMs changed this.

M1 came out before the LLM rush, though

Re: Apple M3 Ultra

#697
Not to rain on the Apple parade, but cloud video editing with the models running on H100s that can edit videos based on prompts is going to be vastly more productive than anything running locally. This will be useful for local development with the big Deepseek models though. Not sure if it's worth the investment unless Deepseek is close to the capability of cloud models, or privacy concerns overwhelm everything.

Re: Apple M3 Ultra

#698
post #280

Earlier quoted context omitted.

Haven't the Max/Ultra type chips always come much later, close to when the next number of standard chips came out? M2 Max was not available when M2 launched, for example.

An Ultra has never come out after the next gen base model, let alone the next gen Pro/Max model before. M1: November 10, 2020 M1 Pro: October 18, 2021 M1 Max: October 18, 2021 M1 Ultra: March 8, 2022 ------------------------- M2: June 6, 2022 M2 Pro: January 17, 2023 M2 Max: January 17, 2023 M2 Ultra: June 5, 2023 ------------------------- M3: October 30, 2023 M3 Pro: October 30, 2023 M3 Max: October 30, 2023 -------…

I'd also point out that there was a rather awkward situation with M1/M2 chips where lower end devices were getting newer chips before the higher end devices. For example, the 14 and 16-inch MacBooks Pro didn't get a M2 series chip until about 6 months after the 13 and 15-inch MacBooks Air. This left some professionals and power users frustrated.

The M3 Ultra might perform as well as the M4 Max - I haven't seen benchmarks yet - but the newer series is in the higher end devices which is what most people expect.

Re: Apple M3 Ultra

#699
post #604

Earlier quoted context omitted.

never understood the hate on the trash can. Isn't the mac studio basically the same idea as the trash can but even less upgradeable?

The Mac Studio hit a sweet spot in 2023 that the trash can Mac Pro couldn't ten years earlier. It's mostly thanks to the high integration of Apple Silicon and improved device availability and speed of Thunderbolt. The 2013 Mac Pro was stuck forever with its original choice of Intel CPU and AMD GPU. And it was unfortunately prone to overheating due to these same components.

The trash can also suffered from hitting the market right around when the industry gave up on making dual-GPU work.
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