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Apple's just-announced 14-inch and 16-inch MacBook Pro models are now available with up to 96GB of unified memory when configured with the highest-end M2 Max chip.

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Until now, both MacBook Pro models maxed out at 64GB of memory when equipped with the previous-generation M1 Max chip. With the introduction of the M2 Max processor with a 12-core CPU and 38-core GPU, Apple's latest Macs now offer up to an additional 32GB of unified memory capacity.

Otherwise, memory bandwidth is unchanged from the M1 Pro and M1 Max models, remaining at up to 200GB/s for the M2 Pro chip and up to 400GB/s for the M2 Max chip.

Customers can order the new 14-inch and 16-inch MacBook Pro via Apple's online store starting today, with availability beginning Tuesday, January 24. Pricing starts at $1,999 for the 14-inch MacBook Pro and at $2,499 for the 16-inch MacBook Pro.

Article Link: New MacBook Pro Models Max Out With 96GB of RAM, Up From 64GB
 
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Do you really need that much ram with these types of processors? I know there’s someone out there who would need it but for what kind of situations?
Someone could probably use it training a machine learning model. It's massively faster if you can avoid writing anything to a disk, even a super fast SSD.
 
It is easier for Apple to add more memory than to teach their MacOS developers how to clear up previously used memory in processes like "WindowServer" which often uses up to 40GB on my M1 Max.. (Yes I then get "Out of memory error on my 64GB machine...)
 
Do you really need that much ram with these types of processors? I know there’s someone out there who would need it but for what kind of situations?
Visualization that requires large textures and models to reside in memory. I don’t think there’s a single PC laptop that even has an option of 64 gigs of texture memory… and few graphics cards even handle that much. And, that’s before considering compression algorithms.
 
Do you really need that much ram with these types of processors? I know there’s someone out there who would need it but for what kind of situations?
I run a full collection of containerized microservices in Kubernetes on my laptop for development. 32GB was not enough. I was thrilled and immediately upgraded to 64GB when it became an option. It works for my current workload, though I can see how other workloads might need more.
 
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