Basically Apple will be building the perfect computers to run local LLMs.
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A Mac with 1.5TB RAM would be expensive at the best of times. In 2027/2028 it might approach $50k, or even get into 6 figures.
It shouldn't be, if Apple gets that import exemption from the Chinese memory manufacturer (ChangXin Memory Technologies) they are asking for. Apparently they're already testing the CXMT chips to put in the phones sold in China, freeing up the orders/stock they've sourced already from "safe" sources, to go into their products sold in the rest of the world.
Smart move if they can finagle it.
Hopefully they can get it through before the fuckwits in the administration understand how effectively it can threaten the big AI players that Trump seems to be sniffing around.
You absolutely BET that he will scuttle any trade deal if it interferes with his own personal agenda WRT his investments in AI.
He's that much a greedy cunt.
Or the more likely option that they get the cheaper ram from China and then double dip and use the ram scarcity excuse for higher prices
apple sells ram at double market rate in the cheapest of times
Big household name gaming companies have devs who burn 20k in tokens A MONTH.
A 50k machine that can run a model locally with 0 monthly costs will pay for itself in 3 months.
With the Apple tax, I'd expect 1.5TB to run you closer to $200k. Enterprise prices are that high today, so if trends continue it's going to be bad
1.5 TB of unified memory sounds less like a computer and more like Apple preparing for the moment your local AI starts asking for a raise. Plot twist: by 2028 the RAM upgrade still costs more than the rest of the machine combined.
Remember this is “unified”, it’s not like you can upgrade, nor is it available in the “cheap” packaging we’re used to.
You’ll get whatever Apple puts on the SoC, and you’ll be happy with it
The upside is that unified memory is genuinely different from traditional RAM. The CPU, GPU and Neural Engine all share the same memory pool, so data doesn’t need to be copied back and forth. That reduces latency, improves efficiency and lets AI models, graphics and other workloads access much larger datasets. It also uses less power and saves board space. The downside is obvious: because it’s integrated into the chip, you have to choose the right amount upfront, since it can’t be upgraded later.
Ya, these high memory amounts and ever increasing memory bandwidth are heavily (but not only) targeting people wanting to run local large AI models like a full deepseek on their machines.
You might not be able to train as well on them as NVIDIA + CUDA, but for local inference, they're an alternative to NVIDIA and more reasonably priced for the model sizes you can run, and each iteration they get better as the bandwidth increases.
Apples memory has never been cheap, it's always been a very expensive upgrade.
I slowly turn to dust as I recall cracking open 2013 MacBook Pros and just putting more memory in.
The memories of loading up the G3 with SDRAM so I can fiddle with Photoshop 5, lost like tears in the rain.
Plot twist: by 2028 the RAM upgrade still costs more than the rest of the machine combined.
Won't at least China have production capacities ready by then that make the price drop?
Nope, it‘ll take several years to catch up.
https://feddit.org/post/32576427
Their DDR5 chips are 30% more expensive, but they are willing to sell 10% below market rates.
The article says that they won't catch up in 2027 but I expect China to pour in resources to catch that opportunity.
Doesn't big companies try open models. Microsoft was testing deepseek.
Everyone is. Open weight and source is the way to go in my opinion.
Can I load all of GTA 6 into RAM?
Yeah, but can it run Crysis?
There's going to be a massive boom in local llms if we get there. Already I have replaced ~80% of my paid token usage with a small local model running on my 64gb macbook pro, but being able to run a full-fidelity multi-hundred-billion parameter llm locally would be a game changer for my use case at least.
You'll only be able to afford 640KB, but it CAN go to 1.5TB!
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