Apple has recently overhauled its entire M-Series chip plans, scrapping the launch of the M6 Pro and M6 Max processors and jumping straight to the M7 series. While the base M6 SoC is expected to launch, Apple is moving into the M7 series without the M6 Pro/Max variants, with plans to offer some...
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.