Modern AI chips rely on several specialized inputs: advanced logic wafers that perform the core computation, high-bandwidth memory (HBM) that stores data and feeds it to the compute engines at high speeds, and advanced packaging that integrates logic and memory together. 1 NVIDIA's data center revenue hit $115. 2B in FY2025 (+142% YoY), but market share is projected to decline from 86% to ~75% by 2026 as custom ASICs scale. 2 Hyperscalers are spending $380B+ on AI capex in 2025 while simultaneously building custom chips (TPU, Trainium, Maia, MTIA) that offer 40-65%. What is generating all the craze is that AI is going from being able to only perform programmed, predictive tasks to it being able to put things in context and generate conclusions. The first wave of AI was learned perception and inference, like recognizing images, understanding speech, and. These include GPUs, custom AI application-specific integrated circuits (ASICs) used by hyperscalers and cloud service providers (CSPs), AI-capable central processing units (CPUs), and other AI ASICs developed by both AI chip-focused startups and large vendors. AI compute capacity is growing exponentially. Unlike previous waves driven by isolated breakthroughs, this phase is characterized by full-stack, cross-layer coordination—spanning advanced process nodes, packaging, memory. Based on our experience running AIMultiple's cloud GPU benchmark with 10 different GPU models in 4 different scenarios, these are the top AI hardware companies for data center workloads. Follow the links to see our rationale behind each selection: Revenue & volume leader.