Why GPUs Are Driving the AI Boom: Architecture, Market Trends, and Future Outlook

Industry Overview

GPU Definition

GPU (graphics processing unit) is a microprocessor specialized for image and graphics calculations in personal computers, workstations, game consoles, and some mobile devices. It is the processor of a graphics card, which assists the CPU in processing images and delivering them to the display.

Parallel‑Computing Advantages

GPU, ASIC, and FPGA are the main compute chips. GPU originally served graphics rendering but has expanded to general‑purpose computing (GPGPU). Its many simple cores excel at parallel mathematical and geometric calculations, offering higher throughput than CPUs, though power consumption remains a drawback.

GPU Application Segments

GPU variants include PC GPUs (discrete and integrated), server GPUs for AI training, inference, and high‑performance computing, and automotive GPUs for on‑vehicle AI inference. Discrete GPUs have dedicated memory and higher performance, while integrated GPUs share system memory and consume less power.

Core Functions

Graphics Rendering : GPU performs vertex shading, shape assembly, rasterization, texture mapping, and pixel processing to generate final images.

General‑Purpose Computing (GPGPU) : Since 2003, GPUs have been used for scientific and AI workloads, supporting INT8, FP16, FP32, and FP64 precision.

API Layer : APIs such as OpenGL, DirectX, and CUDA provide a programming interface between GPU hardware and applications, simplifying development and enabling high‑performance rendering and compute tasks.

CUDA and GPGPU Evolution

CUDA, introduced by NVIDIA in 2007, abstracts GPU hardware into a unified programming model, dramatically reducing the difficulty of parallel programming and enabling widespread AI and scientific applications.

Key Factors Influencing GPU Performance

Micro‑Architecture Design

Performance parameters include micro‑architecture, process node, number of shader processors, stream processors, memory capacity, bus width, bandwidth, and core frequency. Micro‑architecture is the primary driver of performance gains, affecting frequency, power efficiency, and overall compute capability.

Hardware Components

Stream Processors : Basic compute units, often called CUDA cores, handling integer and floating‑point operations.

Texture Mapping Units : Perform texture sampling and mapping onto 3D models.

Rasterizer : Converts 3D geometry into 2D pixel data, handling depth, shading, and post‑processing effects.

Ray‑Tracing Cores : Compute realistic lighting, reflections, and shadows.

Tensor Cores : Accelerate AI inference and DLSS rendering, providing mixed‑precision matrix operations.

Market Analysis

Global GPU Market Size

According to Verified Market Research, the global GPU market was $25.4 billion in 2020 and is projected to reach $246.5 billion by 2028 (CAGR 32.9%). The 2023 market is estimated at $59.5 billion.

PC Graphics Card Segment

In 2022 Q2, PC GPU shipments fell 34% due to macro‑economic slowdown, cryptocurrency mining impact, and inventory clearance. Market share: NVIDIA 62%, Intel 18%, AMD 20%.

Data‑Center and AI Server Demand

GPU adoption in data centers fuels AI training, inference, and high‑performance computing. Omdia projects AI server market growth from $2.3 billion (2019) to $37.6 billion (2026) (CAGR 49%). NVIDIA dominates the accelerator market, with AMD and Intel holding smaller shares.

Industry Chain and Competition

GPU Supply Chain

The GPU value chain consists of design, manufacturing, and packaging. Supply models include IDM, Fab+Fabless, and pure Foundry.

Competitive Landscape

The GPU market is dominated by NVIDIA, Intel, and AMD. In 2022 Q2, market shares were NVIDIA 62%, Intel 18%, AMD 20% for PC GPUs; NVIDIA 80% and AMD 20% for discrete GPUs.

Domestic Chinese GPU Initiatives

Chinese startups such as 璧韧科技, 摩尔线程, 芯动科技, and 天数智能 are emerging, often licensing IP from Imagination or other vendors, indicating a growing domestic ecosystem.

US Export Restrictions and Mitigation

Short‑term, customers can use mid‑range NVIDIA or AMD GPUs not subject to bans. Long‑term, domestic GPUs are expected to replace high‑end foreign chips, though software compatibility (e.g., CUDA) may pose challenges.

Reference: 慧博资讯