Market research firm TrendForce predicts a new wave of AI-server expansion in 2023, with global shipments of AI servers expected to grow by 38.4% YoY, bringing the total to approximately 1.2 million units worldwide.



By 2026, AI servers are set to constitute 15% of the total. This tremendous growth is driven by tech powerhouses equipped with GPUs, FPGAs, and ASICs, designed to service a diverse range of markets.

TrendForce has updated its Compound Annual Growth Rate (CAGR) forecast for AI server shipments from 2022 to 2026, now projecting an ambitious 22%. Similarly, the AI chip market is set to witness a phenomenal increase of 46% in 2023.

NVIDIA dominates the AI server market, commanding a 60-70% market share, followed closely by the independently developed ASIC chips by Cloud Service Providers (CSPs). Three factors underpin NVIDIA's success: the high demand for its A100 and A800 models, the lucrative nature of high-end GPUs, and the sustained popularity of AI computations and ChatBots across professional sectors.

Demand for High Bandwidth Memory (HBM), a high-speed RAM interface used in advanced GPUs, is also expected to surge due to the growing demand for top-tier GPUs, including NVIDIA's A100 and H100, AMD's MI200 and MI300, and Google's proprietary TPU. TrendForce anticipates a staggering 58% YoY increase in HBM demand for 2023, with a further 30% increase projected in 2024.

This projection indicates the growing prevalence and adoption of AI technologies in various industries and applications.

To achieve this, several factors may come into play:

1. Increasing demand for AI technologies: Industries such as healthcare, finance, retail, and manufacturing are increasingly leveraging AI for tasks such as data analysis, predictive modeling, and automation. This rising demand for AI capabilities drives the need for dedicated AI servers.

2. Advancements in AI hardware: The development of specialized hardware, such as graphics processing units (GPUs) and application-specific integrated circuits (ASICs), designed to accelerate AI workloads, is making AI servers more efficient and powerful.

3. Evolving AI algorithms and models: As AI algorithms become more sophisticated and complex, the computational requirements for training and running these algorithms necessitate high-performance servers specifically tailored for AI workloads.

4. Cloud adoption: Many organizations are turning to cloud-based AI services, leading to the deployment of AI servers in data centers and cloud environments to meet the demands for AI processing and inference.

One key aspect contributing to the rise of AI servers is the need for high-performance computing to handle complex AI tasks, such as deep learning, natural language processing, computer vision, and recommendation systems. These workloads require significant computational resources and benefit from dedicated AI-optimized hardware configurations.

Moreover, the projected growth in AI servers aligns with the broader expansion of AI-driven applications and services across industries. For instance, in healthcare, AI is being applied to medical imaging analysis, drug discovery, and patient diagnosis, necessitating high-performance AI servers to process and analyze large volumes of data.

In the automotive industry, AI powers autonomous driving systems, which rely on real-time data processing and decision-making, driving the need for specialized AI servers with low-latency capabilities.

Furthermore, the increasing adoption of edge AI, where AI processing occurs at the edge devices rather than centralized data centers, may also contribute to the demand for AI servers tailored for edge computing environments.

This growth in AI server adoption also underscores the importance of energy efficiency and sustainability in data centers, as AI workloads can be highly demanding in terms of power consumption. As a result, the development of energy-efficient AI server architectures will likely become a focus area for hardware manufacturers and data center operators.

Overall, the rise of AI servers represents not only the technological advancements in AI hardware but also the increasing reliance on AI for critical business functions, research, and innovation. It reflects the ongoing evolution of infrastructure to support the next generation of AI-driven applications and services.