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Apple M7 Ultra Chip Planned With Up to 1.5 TB of Unified Memory
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this post was submitted on 14 Jul 2026
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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.
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.
But I bet next they will close it after gaining bigger foothold. I wonder how would you prevent this.
I don’t think open-weight models can be prevented, as ‘everyone’ knows how distillation works these days and, clearly, no one can do anything to stop it.
No turn deepseek and others to proprietary models
Whatever was open will remain open, even if newer change the license.
Yes, but expertise is not there and when they will lead in ai they will turn it into proprietary software. Afterwards no-one will know how to develop it further.
At least I think so.
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.
How dare you steal hundreds of dollars by installing contraband ram!