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  • Not just the high end stuff. The Neo is sold out until late September on the budget end, it seems like it is a smash for HS and college kids.

    I hope Apple can take all this cash and do some stability releases like they used to do, bugs around things like Family Sharing, the painful "update" to Settings App, etc could all use a lot of love.

    by Scubabear68
  • Huh, glad I grabbed my Neo two weeks ago. It's the "top" spec version, but still a good bit less than a MBA - seemed like a pretty reasonable replacement for the M1 iPadPro it replaced (wanted to go back to a normal laptop vs tablet).
    by alistairSH
  • It's a massive bummer for me because I'm on an old 2015 iMac and was looking to upgrade soon but buy a fairly heafty machine, but the combined Ram price increases and the fact these still sell like hotcakes has made me reconsider.

    I like to buy high end and then keep it for a long time. I'd love local AI But even with a fairly large spend, these machines won't run that much local AI well and that's now, I'm not sure if in the future I'd want even more RAM, seems to be it is better to budget for an AI sub + a less insane machine.

    by ChildOfChaos
  • Maybe something used to keep you running for a few more years.
    by mixmastamyk
  • Stories about Tim Cook sitting there being surprised by sales are total marketing. They simply ran out of memory chips because of the global shortage, so they repacked supply delays into a nice story about insane hype among AI startups. And hit two birds with one stone by throwing shade at their competitors
    by TimByte
  • Caught off guard has been used a lot this year.

    https://www.macrumors.com/2026/08/30/apple-unexpected-mac-mi...

    https://www.macrumors.com/2026/05/01/apple-was-caught-off-gu...

    https://www.macrumors.com/2026/01/29/apple-on-airpods-pro-3-...

    Recent work in this space has got me looking at using local models for daily use. I’m waiting for people to start dumping some of the previous generation minis on Facebook marketplace or eBay so I can pick one up. Of course there’s also other options. I’m still hammering out my requirements and what I want to do besides putz around.

    by magicmicah85
  • Two of those links were Macs. No surprise. Macbooks selling like hotcakes due to AI. Not just local LLMs but also just really efficient *nix machines that AI agents love to use for productivity.
    by aurareturn
  • I really hope with Ternus taking the helm Apple starts to remember that it has products outside of iPhone.
    by paxys
  • I wish they would stop with the new-iPhone-every-year nonsense and refocus on quality, fix some bugs… but yeah, not gonna happen
    by catoc
  • So for people who don't understand, there are two markets for Apple hardware in this space:

    1. Running an agent like OpenClaude. The $599 Mac Mini was an insanely good deal for this. I happened to buy a M5 Pro Mac Mini for $999 last year for other reasons. The equivalent is now almost $2000; and

    2. Hardware for running inference on local models. This to me is the far more interesting market because Apple has a real opportunity to disrupt NVidia's stranglehold on the market.

    With current architecture, the largest model you can reasonbly run is the amount of memory on the GPU and is a function of the quantization (eg int4, int8, fp8, fp16, etc) available and the number of parameters. NVidia aggressively segments the market. The most VRAM on a "consumer" card is 32GB on the 5090, which allows you to run ~31B parameter models.

    In comparison, the RTX 6000 Pro has only slightly more CUDA units than a 5090 but has 80GB of VRAM. A few months ago they were $10-11k. Now they're ~$16k.

    Macs use a shared memory architecture. Apple has previously sold Mac Studios with up to 512GB of RAM. Almost all of that memory can be used to hold much larger models without taking a penalty for interconnections between different GPUs or machines. Plus Apple interconnects between computers are actually relatively good by chaining TB5. It's still slow but it's about the best non-enterprise option available.

    But the previous Mac Studios just didn't have the raw FLOPS and memory bandwidth. The M5 Ultras are up to 1.2TB/s of memory bandwidth. M3 Ultra had ~900GB/s. RTX 5090s and RTX 6000 Pros are 1.8TB/s. The current best HBM3 NVidia DC GPUs are at 3.2TB/s IIRC. But the M5 Ultra has a claimed ~4.5x the FLOPS of the M3 Ultra.

    We don't have our hands on these yet but it probably means they are going to be much closer to a 5090. I expect ~50% of a 5090's inference speed. That may sound bad but it's actually really good because a 256/512GB Mac Studio can probably locally run the best Flash models. With NVidia hardware you'll need to spend many tens of thousands for that.

    We'll see what the inference speed is but I expect it to be usable. DeepSeek v4 Flash, for example, will be entirely runnable. We're not at DeepSeek v4 Pro local yet.

    by jmyeet
  • You have the m4 pro right? I thought the m5 pro mac mini was only just announced
    by subarctic
  • > 1. Running an agent like OpenClaude. The $599 Mac Mini was an insanely good deal for this.

    I still have zero clue how "Buy a $599 Mac Mini to have a sandboxed LLM API caller" became the default. If you're not doing local inference and don't need to inject into iMessage or iCloud, all you need to run openclaw-style harnesses that call external APIs is a Raspberry Pi 4B, an N100, an HTPC, or that 10 year old laptop sitting in your desk.

    by jubilanti
  • Mac Mini's were really nice HTPC candidates, too, before the AI boom. Like all things genuinely useful and affordable, they were snatched from the hands of normal consumers by a bunch of schmucks chasing the latest gold rush.
    by AdmiralAsshat
  • This is just so incredibly disrespectful to so many people.
    by neverrroot
  • I need a new little Mac for my music studio, currently an M2 MacBook Pro. I thought I'd be fun to experiment with some local models as well. Well, let's price up an M5 Pro. $3,019 with 64GB RAM and a 1TB HD. Three thousand American dollars for a Mac Mini. Beefy spec for sure but not comically so.

    Frankly even the entry price is a bit high - I remember buying one for my son a few years ago (M1 mini) and it was a few hundred; now we're up to $900 for the base model.

    by scrumper
  • Isn’t a Mac mini annoying to use as an HTPC? You have to deal with a remote, software, and a full OS, compared with an Apple TV, which has a good remote and is optimized for TV use.
    by ThreeFinger
  • Our Blessed Homeland / Their Barbarous Wastes
    by procgen
  • It's fun to see that even an extremely large company can find unexpected product market fit [0]. Per this article, "The company reportedly did not possess an engineering team dedicated to business customers or staff focused on developer relations, and lacked an enterprise AI strategy." That sounds insane in retrospect, but I think there's just inherent uncertainty in what people actually need and will use things for.

    [0]https://pmarchive.com/guide_to_startups_part4.html: "In a great market—a market with lots of real potential customers—the market pulls product out of the startup... The product doesn’t need to be great; it just has to basically work."

    by setgree
  • > "The company reportedly did not possess an engineering team dedicated to business customers or staff focused on developer relations, and lacked an enterprise AI strategy"

    This is clearly a mis-statement, they have a whole annual conference for developers. Maybe they mean specifically AI devs.

    by ghostly_s
  • No ‘staff focused on developer relations’ is entirely unsurprising based on what I see from the outside.
    by 1over137
  • It's also fun to see how many people here believed this was all some clear deliberate strategy in the first place rather than an accident.
    by DannyBee
  • Maybe a bit of hindsight bias / the outside view here, but I feel like they're completely asleep if they didn't anticipate strong demand for this specific use case.
    by SaltyBackendGuy
  • Was this the case in the past?

    My vibes were that Apple wound down the “actual work” side of their operations (including machines like Xserve), because Ives couldn’t handle the unsexiness and unpredictability of business requirements in hardware.

    He was self-indulgent and only wanted to work on things that “vibed” with him, rather than what the customers needed. It’s easy to be creative when you get to do what you want to do, it’s hard when you have hard constraints.

    by xattt
  • You should listen to the podcast Acquired, specifically Nvidia and then Jensen Huang. They basically lucked into AI. Some researcher was using Nvidia gaming cards, and reached out to them about questions on CUDA. That email eventually turned them into a trillion dollar question.
    by yardie
  • There is a lot of "AI demand" that isn't just running inference on an LLM whose weights you downloaded.

    I'm training a model using reinforcement learning with self-play. I can and do use vast.ai when scaling but for experiments it's far faster, and cheaper, to run it locally until the bugs are all figured out. Just provisioning a new instance and copying the relevant checkpoints and things can take 25 minutes. It's zero locally.

    by alasdair_
  • I’m doing the same!

    Do you find that CoreML manages to fill up your drive with so many tiny files that a reboot takes hours to clean them up? I keep meaning to get my friends still inside the spaceship to file a radar about that.

    What game are you building?

    by mercutio2
  • What are you training on using self play? Like alpha go? Curious what your setup is like .
    by ashish01
  • Same, but with vision models. Unfortunately, I might be at my limit locally. I have three models that I'm using to find and identify objects in pictures. The largest dataset and model now takes about 8 hours per epoch on my Mac M4 with 16G memory.
    by jtap
  • > Just provisioning a new instance and copying the relevant checkpoints and things can take 25 minutes.

    Modal significantly improves this. Highly recommend.

    by ronfriedhaber
  • Are you training something so big you need that much unified RAM though?

    If you can fit it on a GPU, and especially for training, it is so much quicker than a Mac.

    by jmalicki