NVIDIA, RTXs, H100, and more: The Evolution of GPU

AI hardware evolved from early theoretical neural networks and machines such as Rosenblatt’s Mark I Perceptron and the JOHNNIAC computer, which supported landmark but limited pattern-recognition and symbolic-reasoning programs, into increasingly specialized systems for large-scale machine learning. CPU-based optimizations, including fixed-point arithmetic, SIMD instructions, and cache improvements, initially accelerated neural networks, but GPUs proved far more effective for the matrix-heavy computations underlying deep learning. NVIDIA’s introduction of CUDA in 2006 made general-purpose GPU programming more accessible, while architectures from Tesla and Fermi through Kepler, Pascal, Volta, Ampere, and Hopper added features such as improved parallelism, memory bandwidth, mixed-precision computing, interconnects, and tensor cores. GPU acceleration enabled breakthroughs including large-scale unsupervised learning, high-performing convolutional networks, and AlexNet’s influential ImageNet result, while later hardware such as the V100, A100, and H100 supported increasingly large AI models. Google developed TPUs as application-specific chips based on systolic arrays to efficiently perform tensor operations, deploying them in internal services, AlphaGo, and cloud offerings. Alongside data-center accelerators, consumer GPUs such as the RTX series expanded access to local AI development, and the field now includes competing specialized hardware from companies including Apple, Microsoft, and AWS.

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