@manateelazycat: Bloomberg reported that Apple plans to ship 1.5TB of RAM next year. I've already thought of a joke for next year: A: How big is your hard drive? B: 1.5TB. A: Noob, I mean RAM, not hard drive. B: I said RAM.

X AI KOLs Timeline News

Summary

Bloomberg reported that Apple plans to launch devices with 1.5TB RAM next year, and netizens are joking about the future confusion between RAM and storage.

Bloomberg reported that Apple plans to ship 1.5TB of RAM next year. I've already thought of a joke for next year: A: How big is your hard drive? B: 1.5TB A: Noob, I mean RAM, not hard drive. B: I said RAM🤡
Original Article
View Cached Full Text

Cached at: 07/13/26, 07:59 PM

I heard Bloomberg reported that Apple plans to have 1.5T memory next year.

I’ve already thought of next year’s meme:

A: How big is your hard drive?
B: 1.5T
A: Noob, I’m asking about memory, not hard drive.
B: That’s what I meant, memory🤡

Similar Articles

@berryxia: Apple has been betting on on-device models all along! Unified architecture memory is the natural habitat for on-device models! Unified memory means memory is VRAM. We are seeing more and more excellent on-device models emerge. OpenBMB released MiniCPM-V 4.6, a 1.3B multimodal model. After reading it…

X AI KOLs Timeline

OpenBMB released MiniCPM-V 4.6, a 1.3B parameter multimodal model. Using high-resolution visual processing and efficient compression, it achieves fast inference on consumer hardware and mobile phones, outperforming larger models. It is fully open-source and supports multiple inference and quantization frameworks.

@YRSM_Simon: Jensen's precision cuts so sharp it makes your teeth itch. NVIDIA promotes DGX Station: 748GB unified memory. Sounds like it crushes everything—4× RTX PRO 6000's 384GB? Not enough. But look closer—748GB = 252GB HBM3e + 496GB …

X AI KOLs Following

Reveals that the 748GB unified memory advertised for the NVIDIA DGX Station actually only has 252GB of high-speed HBM available. The remaining 496GB of slow LPDDR5X is essentially useless for large model inference, reflecting NVIDIA's precise product differentiation strategy.

@PandaTalk8: These test results are stunning. The original poster tested the DS4 inference engine written in C by @antirez, and local deployment seems incredibly fast. The good news is that only 128GB of RAM is needed to run a local model equivalent to GPT-4o. The bad news is that you need a MacBook Pro with 128GB of RAM.

X AI KOLs Timeline

This article reports on tests of the DS4 inference engine written in C by @antirez, noting its impressive speed when running a GPT-4o-equivalent model on a MacBook Pro with 128GB of RAM.