So long as you don’t mind that it's soldered down
For instance, with the announcement of Qualcomm's Snapdragon 8 Gen 3 system on chip this week, the silicon slinger is betting on a future where customers run machine learning and large language models, like Meta's Llama 2 or Stable Diffusion, entirely on their personal devices.
Most GPUs and accelerators used to run AI workloads use speedy GDDR or high-bandwidth memory modules. However in a slim laptop, tablet, or smartphone this isn't always practical, and the CPU, GPU and other co-processors must often share a common pool of DDR5. One of the techniques to prevent bandwidth from becoming a bottleneck is co-packaging memory alongside their compute dies. Apple's M-series processors are a prime example of that approach, with the memory modules on the same die as the CPU and co-processors.— for the moment, its most powerful notebook SoC — can deliver 400 GB/s of memory bandwidth to the CPU and GPU.
If Apple were to move to Micron or SK-Hynix's latest 9,600 MT/s memory, the company might just be able to eke out another 200GB/s of bandwidth.to be working on a version of its Meteor Lake processors with on-package LPDDR memory. However, it's not in space constrained mobile devices that we're seeing chipmakers go this route. Nvidia's 144-core Grace CPU SuperchipOne of the downsides to LPDDR memory is you can't really upgrade the device by tossing in a higher capacity SODIMM.
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