Why Accelerators Matter
Why specialized hardware is essential (not optional) for AI at scale
What it is
Accelerators (GPUs, TPUs, and emerging custom chips) enable AI at scale through massive parallelism. Transformers can be structured so that most computation happens in large matrix multiplications, which decompose perfectly into thousands of independent operations that accelerators can run simultaneously.
A CPU might have 32 cores executing complex instructions. An H100 has 16,896 CUDA cores running simple operations simultaneously. For the specific math AI training requires, this is thousands of times faster than a CPU.
The key insight: scaling laws show models get reliably better with more compute, and the only way to reach required compute levels with current technology is massive parallelism on accelerators.