Locator: 49469TPUS.
OpenAI: ChatGPT will be "embedded" in the Chrome's default browser.
- Google (Alphabet) is up 2% in early morning trading
- jargon: TPUs.
Jargon -- TPUs:
AI prompt:
Does Apple use (or is there even a place) for TPUs in Apple's AI architecture?
Reply: no.
Excellent question — and very insightful, because it touches the core of Apple’s unique approach to AI hardware design.
TPUs (Tensor Processing Units) are Google-designed ASICs (Application-Specific Integrated Circuits) optimized for tensor math — the matrix operations that dominate deep learning workloads. They’re especially good at:
Massive matrix multiplications
Quantized (e.g., 8-bit) neural network inference
Large-scale training efficiency in Google Cloud
They were built for data-center scale AI, not personal devices.
Apple does not use TPUs — and there’s no place for them in Apple’s current architecture.
Apple has built its own dedicated AI accelerator hardware called the Neural Engine.
Introduced in 2017 with the A11 Bionic chip (in iPhone X).
Integrated into A-series (iPhones/iPads) and M-series (Macs) chips.
Designed for on-device machine learning — not large-scale training.
The Neural Engine is Apple’s equivalent to a TPU, but for the edge, not the cloud.
Apple avoids external TPU-type data centers for most user interactions.
It runs on-device inference with Neural Engines wherever possible.
For heavier AI (e.g., Apple Intelligence summarizations, complex Siri tasks), Apple offloads to a private Apple Cloud, running servers built with Apple Silicon.
Apple doesn’t use TPUs, GPUs from NVIDIA, or Google infrastructure — everything is vertically integrated.
So TPUs don’t fit Apple’s model — they’re Google’s solution for massive shared compute, whereas Apple focuses on personalized, privacy-preserving on-device compute.
Summary:
