The AI server market is projected to reach US$245 billion in 2025 and is expected to grow to US$523 billion by 2030, driven by rising demand for Generative AI (Gen AI) tools like ChatGPT, Perplexity, and Claude, ABI Research said in a report.
Enterprises increasingly deploy AI models in-house, creating a surge in processing power requirements for model training and inference. This growth has prompted data centers and cloud providers to invest in advanced server and cooling technologies, including liquid cooling, to handle the high energy demands of GPUs from NVIDIA and AMD.
Major tech players such as AWS, Google, Microsoft, Meta, and xAI are expanding large-scale GPU and ASIC clusters, while niche providers offer AI-optimized accelerator platforms.
Despite this momentum, the market faces challenges including power constraints, tariff policies, and competition from China, pressuring server OEMs to innovate rapidly. Legacy server solutions struggle to meet these unprecedented compute demands, driving a strategic shift toward AI-optimized infrastructure.
AI server OEMs are evolving strategies to anticipate next-generation AI workload demands, focusing on pre-validated large AI clusters, infrastructure utilization, professional services, rapid deployment, and cost efficiency. Leveraging expertise in infrastructure management is helping OEMs differentiate in a competitive market.
Supermicro: Rapid Deployment and Modular Customization
Supermicro is known for its rapid deployment capabilities and highly modular designs. Leveraging Arm-based CPUs and NVIDIA Blackwell GPUs, the vendor also offers liquid-cooled racks capable of handling densities up to 250 kW. Its Building Block Solutions allow high configurability without relying on OEMs or ODMs. Partnerships with Submer and Castrol enhance its vertical cooling solutions, making Supermicro a strong choice for organizations needing quick lead times and dense AI compute deployments.
Dell: Enterprise-Scale Reliability and HPC Expertise
Dell brings decades of High-Performance Computing (HPC) experience to the AI server market. Offering a broad spectrum of configurations—from CPU-only setups to large-scale training systems with NVIDIA NVL72 and AMD Instinct GPUs—Dell’s servers cater to diverse AI workloads. The company supports Agentic AI initiatives like Cohere and delivers a full-stack, low-risk option for enterprises. Its flexible deployment options include direct sales, global resellers, OEM/ODM partnerships, and APEX GPU-as-a-Service, enabling both on-premises and cloud-connected AI workloads.
HPE: Open-Standard Supercomputing
HPE’s AI server strategy leverages its supercomputing pedigree and open standards advocacy. The vendor integrates Cray systems, Slingshot interconnects, and supports accelerators such as GR200, MI325X, Gaudi3, and Arm-based CPUs. Through subscription-based GreenLake offerings, HPE combines flexibility with validated AI supercomputing systems. Its open fabric approach and unique networking technologies make HPE ideal for organizations seeking scalable, open, and performance-optimized AI environments.
Lenovo: Vertically Integrated Manufacturing
Lenovo leverages a fully integrated supply chain to deliver AI servers optimized for orchestration and component quality. NVIDIA B200-based platforms support up to 160-GPU clusters, although early launches lacked AMD and Intel accelerators. Lenovo offers both subscription-based TruScale and traditional financing models through global resellers. Its in-house hardware and cooling expertise make Lenovo a strong contender for enterprises valuing manufacturing consistency and system reliability, though GPU diversity is currently limited.
Cisco: Networking-First AI Infrastructure
Cisco focuses on AI infrastructure manageability, delivering solutions with NVIDIA H200 and AMD MI300X accelerators. Using a conservative SKU rollout, Cisco emphasizes high-touch customer engagement and security-conscious deployments. The FlexPod model supports customized AI implementations, while the Intersight platform enables global monitoring and management. Cisco’s YouTube video shows it appeals to organizations that prioritize integration, networking, and operational oversight alongside AI compute performance.
ABI Research projects AI server shipments will grow from 3.2 million units in 2024 to 8.2 million by 2030, a 17 percent CAGR. Growth will primarily come from Tier One CSPs, expected to account for 46 percent of shipments by 2030, followed by enterprise private clouds, neocloud providers, and community clouds. Enterprise private clouds will see steady growth, while GPU-as-a-Service offerings from neoclouds could generate over US$65 billion in annual revenue by 2030, largely dependent on NVIDIA hardware.
The AI server market remains concentrated in the U.S., China, and Europe, with hyperscalers investing heavily in AI infrastructure (over US$260 billion in Capex in 2024 alone). North America leads in shipments and revenue, followed by China, Europe, and growing opportunities in the Middle East.
AI workloads are diversifying:
Medium and heavy inference workloads for Gen AI are growing at a 43 percent CAGR, demanding high-performance GPU systems.
Agentic AI training and fine-tuning is projected to grow at 68 percent CAGR, relying on NVL72 and 64-GPU rack-scale systems.
Light inference workloads, including NLP and recommendation systems, remain significant, supported by cost-efficient CPU platforms.
The AI server market is being driven by generative and customized AI workloads, with enterprises fine-tuning large language models for industry-specific applications. High-performance systems such as NVL72 and 64-GPU rack-scale servers are in strong demand for proprietary model deployment and low-latency inference.
Tier One hyperscalers remain the largest source of demand, with U.S. and Chinese providers investing over US$260 billion in AI infrastructure CAPEX in 2024 to support large-scale training and inference.
Medium-to-heavy inference workloads, including copilots, autonomous agents, and multimodal applications, are growing at a 43 percent CAGR, requiring dense compute racks and high-bandwidth interconnects.
Second-tier cloud and neocloud providers are differentiating themselves by adopting modular, Open Compute Project (OCP)-friendly servers, expanding opportunities beyond hyperscalers and creating new Total Addressable Markets (TAMs) for AI server OEMs.
The AI server market faces critical risks that could impact growth. Power constraints are a major challenge, as AI racks already draw 50–150 kW and could reach 1 MW, raising operating costs and complicating ESG and net-zero compliance. Trade restrictions and tariffs, particularly on components from Taiwan and China, may increase production costs for Western OEMs.
Emerging silicon challengers like Cerebras, Groq, and SambaNova are bypassing traditional OEMs by offering AI hardware through their own cloud services, threatening total addressable market (TAM) share. Additionally, Chinese market decoupling due to export controls is driving local alternatives such as Huawei’s Ascend platform, limiting opportunities for Western OEMs in the region.
Rajani Baburajan
