The global artificial intelligence (AI) server market is expected to remain dominated by servers based on graphics processing units (GPUs) this year, but application-specific integrated circuits (ASICs)-based models are set to see faster growth in shipments, the Market Intelligence and Consulting Institute (MIC) said.
As major cloud service providers (CSPs) continue to train large language models for machine learning, outlays on AI data centers and high-density GPU server racks remain a priority, MIC said.
CSPs would continue relying on GPU ecosystems when offering computing resources to customers, since most enterprises and developers remain accustomed to working within GPU environments, MIC information and communications technology section deputy director Chris Wei (魏傳虔) said last week.
Photo courtesy of Hon Hai Precision Industry Co
However, demand for servers powered by ASICs is growing as the market’s focus gradually shifts toward inference, Wei said.
While AI training is akin to preparing materials in a centralized factory — capital-intensive and large in scale — inference resembles bringing those materials back on premises, where use is more distributed and steady.
Such a pattern makes shipment growth for ASIC-based servers more likely to outpace that of GPU-based models this year, Wei said.
Shipments of servers used for AI inference are expected to be about four times higher than those used for training this year, highlighting the role ASICs could play.
ASICs are well-suited to inference demand, as they prioritize efficiency and cost control, Wei said.
Inference workloads are relatively fixed and suitable for specialized designs that can complete specific tasks with lower power consumption, he said.
MIC expects Taiwanese manufacturers who already produce GPU servers to expand into the ASIC segment smoothly, Wei said.
The key to future competition for these manufacturers lies not only in securing orders tied to specific chips, but in whether they can build system integration capabilities and flexible production lines for both GPU and ASIC-based platforms, he said.
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