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The thing that's special about transformers specifically is that the weights are so large that you can't fit them all on chip. And so what happens is that you spend most of your time just loading weights from memory rather than doing computation. And so the key insight behind Etched is that if you hardcode the transformer architecture into silicon, you can do a lot of tricks to basically never have to load the weights from memory.

Invest Like the Best with Patrick O'Shaughnessy
4d ago

The interesting thing about transformers is that they're actually not that computationally intensive if you look at the raw math. The reason GPUs are inefficient for inference is that GPUs are optimized for training, where you do massive parallel matrix multiplications, but inference is fundamentally a different computational problem — you're doing sequential token generation where memory bandwidth is the bottleneck, not compute.

2w ago

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