AI Computing Power Structural Turning Point: Inference Chip Demand Explodes in H2 2026, New Opportunities in Asian Semiconductor Equipment Chain

In 2026, the AI computing power market is undergoing a profound structural change. Over the past two years, the market focus has been on training chips (such as NVIDIA H100) for training large language models (LLMs). However, as large models mature, enterprises are shifting their focus to the inference stage—that is, utilizing trained models to provide actual services, such as cloud AI applications and intelligent processing on edge devices. This shift is leading to a significant increase in demand for inference chips, making them the new engine of the AI computing power market. ## I. Differentiation of AI Computing Power Demand: Training Slowdown, Inference Explosion According to data from industry research institutions, the global inference chip market size is projected to reach $150 billion in 2026, a year-on-year growth of 35%, while the training chip market size is approximately $80 billion, with growth slowing to 20%. This differentiation is mainly due to two factors: first, after large model training is complete, enterprises need to deploy a large number of inference chips to support actual applications; second, the demand for inference chips is more dispersed, covering multiple areas such as cloud services, autonomous driving, and intelligent terminals, resulting in a market size much larger than that of training chips. ### 1.1 Large Model Deployment Accelerates, Inference Demand Surges In recent years, the proliferation of large language models (such as GPT-4, Claude 3) has driven demand for inference chips. Enterprises are no longer satisfied with just training models; they want to apply models to real business, such as customer service, data analysis, and autonomous driving. For example, in Microsoft's Azure cloud services, the proportion of inference chips has exceeded that of training chips, becoming the main source of computing power consumption. Similarly, Google's Vertex AI platform has significantly increased investment in inference chips to meet the needs of enterprise customers. Furthermore, the development of edge computing is further driving demand for inference chips. As the number of IoT devices increases, more AI applications need to run on edge devices, such as smart cameras and industrial robots. These devices require inference chips with low power consumption and high energy efficiency, thus the market demand for edge inference chips is rapidly growing. It is predicted that the edge inference chip market size will reach $30 billion in 2026, accounting for 20% of the total inference chip market. ### 1.2 Tech Giants Shift Capital Expenditure to Inference Facing the explosion in inference demand, tech giants are adjusting their capital expenditure structure. NVIDIA, as the leader in AI chips, has shifted some of its capacity from training chips to inference chips. Its latest launch, the L40S GPU, is designed specifically for inference, offering a 40% improvement in performance-per-watt compared to the previous generation, suitable for cloud services and edge computing scenarios. Additionally, NVIDIA has launched dedicated chips for inference (such as TensorRT-LLM) to help enterprises optimize inference performance. Microsoft and Google are also increasing their investment in inference chips. Microsoft's Azure platform has deployed over 100,000 inference servers using NVIDIA A100 and L40S GPUs to meet enterprise customer needs. Google has launched TPU v5p, designed specifically for inference with higher performance-per-watt, suitable for large-scale deployment. These adjustments indicate that the capital expenditure of tech giants is shifting from the training end to the inference end, bringing new opportunities to the Asian semiconductor supply chain. ## II. Asian Semiconductor Supply Chain: Advantages in Advanced Capacity and Packaging Technology In the structural shift in AI computing demand, the Asian semiconductor supply chain is the biggest beneficiary due to its advantages in advanced capacity and packaging technology. Companies like TSMC, SK Hynix, and Samsung are seizing this opportunity by providing high cost-performance chip manufacturing and packaging services. ### 2.1 TSMC: 3nm Capacity Fully Loaded, Advanced Packaging Demand Surges TSMC, as a leading global foundry, has its 3nm process as the preferred technology for inference chips. Since inference chips require higher energy efficiency, the 3nm process offers higher transistor density and lower power consumption, attracting the attention of tech giants. Currently, TSMC's 3nm capacity is fully loaded, primarily producing NVIDIA's L40S GPU and Microsoft's TPU v5p inference chips. Furthermore, TSMC's advanced packaging technology (such as CoWoS) is crucial for inference chips. CoWoS is a 2.5D packaging technology that encapsulates multiple chips to improve bandwidth and energy efficiency. With the increasing bandwidth demands of inference chips, the demand for CoWoS is surging. TSMC has expanded its CoWoS capacity to meet customer needs. It is estimated that TSMC's CoWoS revenue will reach $10 billion in 2026, a year-on-year increase of 50%. ### 2.2 SK Hynix: HBM Demand Soars, Storage Chips Benefit As a leading global memory chip manufacturer, SK Hynix's High Bandwidth Memory (HBM) has become the standard for inference chips. HBM is stacked memory with bandwidth much higher than traditional DDR memory, making it suitable for the high bandwidth needs of inference chips. With the growth in inference chip demand, the demand for HBM is also significantly increasing. SK Hynix's HBM3e is widely used in NVIDIA's H100 and L40S GPUs. According to SK Hynix, the demand for HBM will reach 2 million units in 2026, a year-on-year increase of 60%. To meet demand, SK Hynix has expanded HBM capacity and built new factories in South Korea, Taiwan, and China. Furthermore, SK Hynix has launched HBM4 to further enhance bandwidth and capacity to meet future inference chip demands. ### 2.3 Samsung: Co-development of Storage and Logic Chips As the world's largest memory chip manufacturer, Samsung is also benefiting from the growth in inference chip demand. Its DDR5 memory and HBM3e memory are widely used in inference chips. Additionally, Samsung has launched logic chips (such as the Exynos series) for edge inference devices. Through the co-development of storage chips and logic chips, Samsung holds a significant position in the AI computing market. Samsung's 3nm process is also beginning mass production for inference chips. Its 3nm process offers a better energy efficiency than TSMC's 3nm, which attracts the attention of some customers. Furthermore, Samsung has launched advanced packaging technology (such as X-Cube) to encapsulate logic chips and storage chips to improve energy efficiency. These technologies give Samsung a competitive edge in the inference chip market. ## III Market Reaction: Capital Flow and Stock Performance The structural shift in AI computing demand is reflected in the capital markets. Recently, US stock funds have shifted from training chip-related companies to inference chip-related companies, leading to a divergence in stock performance. ### 3.1 US Stock Capital Flow: Shift from Training to Inference Based on fund flow data, since July 2026, US stock funds have flowed out of Nvidia (leading training chip) towards Asian semiconductor companies such as TSMC and SK Hynix. For example, Nvidia's stock price fell by 5% in July, while TSMC's stock price rose by 8%, and SK Hynix's stock price rose by 12%. This shift indicates that investors have recognized the growing demand for inference chips and are beginning to position themselves in relevant companies. Furthermore, US stock funds are also flowing into edge inference chip companies, such as Nvidia's L40S GPU suppliers and TSMC's CoWoS packaging suppliers. The stock performance of these companies outperforms the market average, demonstrating investor confidence in inference chips. ### 3.2 Asian Semiconductor Stocks: Valuation Reassessment Opportunities The valuation of Asian semiconductor stocks is beginning to be reassessed. In the past two years, companies related to training chips had higher valuations, while those related to inference chips had lower valuations. With the explosion in inference demand, the valuations of inference chip-related companies are gradually increasing. For example, TSMC's Price-to-Earnings ratio increased from 25 times to 30 times, and SK Hynix's Price-to-Earnings ratio increased from 15 times to 20 times. In addition, Asian semiconductor equipment companies benefit from the growth in inference chip demand. For example, the increased demand for EUV lithography machines for 3nm processes benefits ASML. Although ASML is a European company, the increase in orders for its Asian supply chain (such as TSMC, Samsung) drives revenue growth for Asian equipment companies. For instance, China Semiconductor's equipment orders grew by 30% year-over-year, demonstrating the vitality of the Asian semiconductor equipment market. ## IV. Risks and Challenges: Supply Chain Bottlenecks and Geopolitics Despite the structural shift in AI computing demand bringing opportunities, the Asian semiconductor supply chain still faces risks and challenges. ### 4.1 Supply Chain Bottlenecks: Advanced Packaging and HBM Supply Tightness The growth in inference chip demand is leading to tightness in the supply of advanced packaging and HBM. TSMC's CoWoS capacity is full, unable to meet all customer demands. SK Hynix's HBM capacity is also tight, leading to price increases. It is estimated that HBM prices will rise by 20% in 2026, and advanced packaging prices will rise by 15%. These bottlenecks may lead to increased costs for downstream enterprises, affecting their profitability. For example, an increase in the cost of Nvidia's L40S GPU could lead to a decrease in its gross margin. Furthermore, supply chain bottlenecks may lead to order delays, affecting companies' delivery capabilities. ### 4.2 Geopolitical Risks: US-China Tech Competition US-China tech competition remains a significant risk to the Asian semiconductor supply chain. Restrictions on US semiconductor exports to China could lead to Chinese companies shifting orders to other regions. For example, Chinese inference chip companies may turn to TSMC or Samsung for contract manufacturing, leading to further tightening of TSMC's capacity. Furthermore, geopolitical risks may lead to technology restrictions. For example, the US may restrict the export of advanced packaging technology, affecting the technological upgrades of Asian semiconductor companies. Companies need to prepare in advance to mitigate the impact on the supply chain. ## V. Conclusion: Structural Shift in AI Computing Demand Brings New Opportunities to the Asian Semiconductor Supply Chain In the second half of 2026, the structural shift in AI computing power demand from training to inference brings new opportunities to the Asian semiconductor supply chain. Companies such as TSMC, SK Hynix, and Samsung are positioned to benefit most from this trend due to their advantages in advanced capacity and packaging technology. Despite facing supply bottlenecks and geopolitical risks, these companies are expected to seize this opportunity through technological innovation and capacity expansion to revalue their worth. For investors, attention should be paid to companies related to inference chips, such as TSMC, SK Hynix, NVIDIA (inference chip business), and Microsoft (Azure inference services). The performance growth of these companies is expected to exceed market expectations, bringing good investment returns. Simultaneously, attention should also be paid to edge inference chip companies, such as NVIDIA's L40S GPU suppliers and TSMC's CoWoS packaging suppliers, as these companies are expected to benefit from the development of edge computing.