Why GPUs Power Today's AI Large Models: A Complete Technical Overview
The article explains how GPUs, originally built for graphics, have become the engine behind AI large models, detailing their advantages in matrix computation, memory bandwidth and interconnect, and reviewing the leading GPUs such as NVIDIA A100, H100, H200, B200, AMD Instinct MI300X and Intel Gaudi 3.
AI large models are the core of modern AI architectures, and GPUs serve as the engine that drives their training and inference.
GPU (Graphics Processing Unit) was initially designed for graphics rendering, with early applications in game rendering, 3D modeling, video decoding, and image processing.
Because GPUs excel at matrix operations and parallel computation, they naturally became the primary hardware for AI training and scientific computing.
Today, virtually all mainstream AI large models—including ChatGPT, DeepSeek, Qwen, and Llama—rely on GPU clusters for both training and inference.
GPU’s value in large models is threefold: it accelerates training, reducing months of work to days or weeks; it provides high‑bandwidth memory to hold billions of parameters and intermediate activations; and it supports fast inter‑connect technologies that enable efficient multi‑card scaling.
Without GPUs, training costs would soar and timelines would lengthen dramatically, making many advanced models impractical to deploy.
The global AI large‑model compute market is dominated by NVIDIA, with AMD and domestic chips actively catching up.
NVIDIA A100 : data‑center training GPU, used for large‑model training and inference.
NVIDIA H100 : Hopper architecture, ~2000 TFLOPS FP16, 80 GB HBM3, the current flagship for super‑large models.
NVIDIA H200 : larger HBM and higher bandwidth, suited for long‑context training and inference.
NVIDIA B200 : Blackwell architecture, the next‑generation AI infrastructure slated for rollout in late 2024‑2026.
AMD Instinct MI300X : high memory and bandwidth, targeted at AI training and inference.
Intel Gaudi 3 : AI accelerator designed for enterprise AI clusters.
H100/H800 (Hopper) currently serves as the absolute mainstay for global large‑model training, offering 80 GB HBM3 memory and up to 2000 TFLOPS FP16 performance.
Blackwell (B200/GB200) will adopt a dual‑chip package, delivering several times the compute of Hopper and aiming at trillion‑parameter models.
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Mike Chen Rui
Over 10 years as a senior tech expert at top-tier companies, seasoned interview officer, currently at leading firms like Alibaba.
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