AI Large Model GPUs Explained: Parallel Computing, HBM & Chip Comparison

GPU stands for Graphics Processing Unit. Originally designed for graphics rendering tasks such as 3D modeling, lighting calculation, image rendering, and video processing, these workloads share a common trait: they require massive amounts of similar mathematical calculations performed simultaneously.

For example, a 4K image contains millions of pixels. If a CPU processes them sequentially — Pixel 1 → compute, Pixel 2 → compute, Pixel 3 → compute, and so on — efficiency is low. GPUs excel at parallel execution: Pixel 1, Pixel 2, Pixel 3, Pixel 4, Pixel 5 all feed into a parallel compute unit at once. This large-scale parallel computing capability is the core advantage of GPUs.

CPU sequential:
Pixel 1 → compute
Pixel 2 → compute
Pixel 3 → compute
Pixel 4 → compute
...

GPU parallel:
Pixel 1 ─┐
Pixel 2 ─┤
Pixel 3 ─┤→ parallel compute
Pixel 4 ─┤
Pixel 5 ─┘

Why GPUs Became the Core Compute for Large Models

GPUs deliver value in three key areas for large model training and inference:

Accelerate model training. Large model training involves massive matrix multiplications and tensor operations. GPU parallel architecture significantly boosts training throughput and reduces time-to-convergence.

Provide high-bandwidth memory (HBM). Large models contain billions of parameters; training also requires storing activations, gradients, and other intermediate data. This demands both high memory capacity and high bandwidth. GPUs typically equip HBM (High Bandwidth Memory) to feed compute cores rapidly, meeting the needs of both training and inference.

Support multi-GPU high-speed interconnect. A single GPU's compute and memory are limited. Ultra-large models require thousands of GPUs working together. Modern AI GPUs support NVLink and NVSwitch, reducing inter-GPU communication overhead and improving distributed training and inference efficiency.

Mainstream AI GPU Comparison

The following table summarizes the leading AI GPUs on the market as of the article's publication:

NVIDIA H100 (Hopper) — 80GB HBM3, 3.35 TB/s bandwidth, positioned for large model training and inference.

NVIDIA H200 (Hopper) — 141GB HBM3e, 4.8 TB/s bandwidth, an upgrade over H100 focusing on larger memory and higher bandwidth for inference and training.

NVIDIA B200 (Blackwell) — 180GB HBM3e, 8 TB/s bandwidth, next-generation architecture further improving compute, memory, and interconnect for next-gen large model workloads.

AMD MI300X (CDNA 3) — 192GB HBM3, 5.3 TB/s bandwidth, targeting large model training and inference.

AMD MI350X (CDNA 4) — 288GB HBM3E, 8 TB/s bandwidth, next-generation AMD chip pushing memory capacity and bandwidth further.

Google TPU (TPU architecture) — Custom architecture for large model training and inference; specific memory and bandwidth figures not disclosed in the source.

The global AI large model compute market is currently dominated by NVIDIA, with AMD and various in-house custom chips actively catching up.