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Here's the thing most chip articles miss: the "nm" number in modern process node names (TSMC 3nm, Intel 4nm, Samsung 3nm) does not correspond to any physical dimension on the chip. This was true when Intel's 45nm node launched in 2007 and became increasingly disconnected from reality thereafter. TSMC's "3nm" node has a gate pitch (the distance between gates) of approximately 48nm — not 3nm.
GPU acceleration is limited by the fraction of your workload that's actually parallel. Amdahl's Law states: if 90% of a program can be parallelized, the maximum speedup from infinite parallel processors is 10× — not infinite. The 10% sequential bottleneck always limits total speedup. In GPU-accelerated ML training, the actual workload split is typically: 85-95% embarrassingly parallel tensor operations
GPU acceleration is limited by the fraction of your workload that's actually parallel. Amdahl's Law states: if 90% of a program can be parallelized, the maximum speedup from infinite parallel processors is 10× — not infinite. The 10% sequential bottleneck always limits total speedup. In GPU-accelerated ML training