Why the Data Center Processor Market Will Hit $3.7 T by 2030 – AI GPUs & ASICs Lead the Surge

The data center processor market is expected to reach $3.72 trillion by 2030.

Rapid expansion is fueled by the growing demand for high‑performance computing in generative AI applications. In 2024 the market size reached $1.47 trillion and is forecast to grow to $3.72 trillion by 2030. Graphics processors (GPU) and AI‑specific ASICs, the core technologies for generative AI, are delivering double‑digit growth.

Central processing units (CPU) and network processors such as data‑plane processors (DPUs) also hold important positions and maintain steady growth. In the AI‑dominated GPU and AI‑ASIC segment, the FPGA market share is sharply declining and is expected to stabilize in the medium term. The rapid expansion of cryptocurrency markets drives strong demand for crypto‑mining ASICs, which are critical for transaction verification.

Nvidia maintains a leading position in AI competitions, but Google and Amazon Web Services are heavily investing in self‑developed AI ASICs.

Since OpenAI’s 2022 breakthrough in generative AI, the market has been reshaped, greatly benefiting Nvidia’s GPU business. Large‑scale data‑center operators such as Google and AWS are partnering with companies like Broadcom, Micron and Alchip to develop proprietary AI ASICs for greater autonomy.

Start‑ups like Groq, Cerebras and Graphcore are innovating aggressively, sparking a wave of M&A and financing. This AI‑ASIC transformation is prompting a shift toward ARM‑based CPUs, challenging Intel’s and AMD’s long‑standing x86 dominance. Crypto‑mining farms with high‑power cooling solutions are also deploying top‑tier GPUs to enter the AI market.

Chiplet architectures and advanced process nodes are shaping the future of generative AI.

Chiplets play a crucial role in GPUs, CPUs and ASICs, improving yield and enabling larger chips on more advanced process nodes. In 2024 the latest CPUs use 3‑nm technology, while GPUs and AI ASICs remain at 4‑nm, though AWS Trainium 3 is expected to adopt 3‑nm in 2025.

Since 2020, compute power has increased eightfold to meet AI demands, and Nvidia plans to launch the Rubin Ultra inference chip with 100 PFLOPS (FP4 precision) by 2027.

As AI models grow larger, low‑latency, high‑bandwidth memory becomes increasingly critical. High‑bandwidth memory (HBM) is central to solutions from Nvidia, AMD, Google and AWS, while many AI‑ASIC start‑ups such as Groq and Graphcore are developing SRAM‑based processors to boost performance.