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As someone who works in machine learning I can tell almost all non LLM applications of ML have moved to using the same fundamental building blocks as LLMs. Namely, transformers and attention. It’s all matrix math. The hardware has not been specialized to the point of being useless for anything else. Vision models. Audio models. Drug discovery models. World models. All using the same building blocks. All using the same hardware.
Even stepping back from transformers nearly all modern machine learning uses some neural network architecture (CNN, GNN, RNN, autoencoder, state space models, etc) that is fundamentally just matrix math. GPUs are general purpose programming hardware for massive parallel computation which lend themselves to matrix math.
This is a free nugget for you since you’re so clueless. You’re welcome.

As someone who works in machine learning I can tell almost all non LLM applications of ML have moved to using the same fundamental building blocks as LLMs. Namely, transformers and attention. It’s all matrix math. The hardware has not been specialized to the point of being useless for anything else. Vision models. Audio models. Drug discovery models. World models. All using the same building blocks. All using the same hardware.
Even stepping back from transformers nearly all modern machine learning uses some neural network architecture (CNN, GNN, RNN, autoencoder, state space models, etc) that is fundamentally just matrix math. GPUs are general purpose programming hardware for massive parallel computation which lend themselves to matrix math.
This is a free nugget for you since you’re so clueless. You’re welcome.