The Way Out for Consumer Graphics Cards Amid the AI Computing Wave: From Stock Competition to Track Reconstruction
发布时间：2026-01-14 10:47:27
As Memory Chips have become the "hard currency" of the AI era, the consumer graphics card market is undergoing an unprecedented structural transformation. Since 2025, driven by the explosive growth in demand for AI Servers, the prices of core storage components such as DRAM (Dynamic Random-Access Memory), GDDR6 (Graphics Double Data Rate 6), and GDDR7 (Graphics Double Data Rate 7) have soared. Coupled with the fact that HBM (High Bandwidth Memory) production capacity is tilted toward high-end computing power, consumer graphics cards have been caught in a double dilemma of "cost inversion + new product stagnation" — the proportion of memory costs has surged to over 80%, yet terminal prices struggle to keep pace with cost hikes. Numerous solution companies have been forced to suspend new projects, and the market scale continues to shrink.
The core of this industry pain lies in the restructuring of the supply and demand pattern of Storage Chips. As cloud vendors like Google and Meta ramp up investment in AI infrastructure, the Memory demand of a single AI server reaches 8–10 times that of traditional servers. Leading manufacturers such as Samsung, SK Hynix, and Micron have shifted their advanced process capacity to high-end products like HBM3e (High Bandwidth Memory 3 Enhanced) and DDR5 (Double Data Rate 5). This has led to a persistent shortage in the supply of consumer-grade GDDR Memory, with prices surging by over 300% within the year. This structural shortage is not a short-term speculation but an inevitable industry trend driven by the restructuring of AI computing power, which is expected to last until 2028.
Faced with the "siphon effect" of storage costs, the market positioning of consumer graphics cards must move beyond the traditional single gaming track and transform toward "computing power diversification + scenario precision":
1. High-End Market: Bind to Professional Computing Power and Meet AI Implementation Needs
Flagship graphics cards should break away from the gaming hardware positioning and focus on high-performance scenarios such as Local AI Development, professional content creation, and film and television rendering. Products equipped with GDDR7 Memory and high-spec Cache Chips can leverage their powerful computing power to meet developers' needs for AI model training and inference. Meanwhile, they can balance cost and performance through Customized Storage Solutions, escaping the price competition in the consumer market.
2. Mid-Range Market: Target AI Terminals and Tap into Lightweight Scenarios
With the explosive growth in AI PC shipments (accounting for 31% of the global PC market in 2025), mid-range graphics cards can focus on scenarios such as Edge Computing, intelligent cockpits, and lightweight design. Adopting a combination of DDR5 Memory and optimized GDDR6 Memory, they can take "cost-effective computing power + low-power storage" as the core to adapt to terminal needs such as AI assistants and real-time rendering, opening up a new track integrating consumer electronics and automotive electronics.
3. Domestic Replacement: Seize Supply Chain Opportunities and Break Through Storage Bottlenecks
Against the backdrop of restricted supply of overseas high-end computing power products, domestic graphics card manufacturers should collaborate with local Memory Chip Design enterprises to tackle key components such as Low-Power DRAM and Customized GDDR. Through industrial chain collaboration, they can reduce dependence on imported storage. At the same time, focusing on the mid-range market, they can leverage the advantages of "adapting to local scenarios + stable supply chain" to fill the market gap left by the contraction of international manufacturers.
The Industry Trend Is Clear: Storage Determines the Upper Limit, Scenarios Determine the Increment
The future competition of consumer graphics cards is essentially a competition of Memory Chip Adaptability and scenario implementation capabilities. Single-purpose gaming graphics cards that are divorced from the AI computing wave will eventually be eliminated by the market. Only by keeping up with Memory Technology Iteration and binding to incremental scenarios such as AI Terminals, professional content creation, and intelligent automobiles can they break through and gain a foothold in the industry pattern of high storage costs.
This market reshuffle triggered by Storage Chips is both a challenge and an opportunity. Consumer graphics cards must break the path dependence and take "computing power as the core, storage as the wing, and scenarios as the anchor" to occupy a place in the industrial restructuring of the AI era.
The core of this industry pain lies in the restructuring of the supply and demand pattern of Storage Chips. As cloud vendors like Google and Meta ramp up investment in AI infrastructure, the Memory demand of a single AI server reaches 8–10 times that of traditional servers. Leading manufacturers such as Samsung, SK Hynix, and Micron have shifted their advanced process capacity to high-end products like HBM3e (High Bandwidth Memory 3 Enhanced) and DDR5 (Double Data Rate 5). This has led to a persistent shortage in the supply of consumer-grade GDDR Memory, with prices surging by over 300% within the year. This structural shortage is not a short-term speculation but an inevitable industry trend driven by the restructuring of AI computing power, which is expected to last until 2028.
Faced with the "siphon effect" of storage costs, the market positioning of consumer graphics cards must move beyond the traditional single gaming track and transform toward "computing power diversification + scenario precision":
1. High-End Market: Bind to Professional Computing Power and Meet AI Implementation Needs
Flagship graphics cards should break away from the gaming hardware positioning and focus on high-performance scenarios such as Local AI Development, professional content creation, and film and television rendering. Products equipped with GDDR7 Memory and high-spec Cache Chips can leverage their powerful computing power to meet developers' needs for AI model training and inference. Meanwhile, they can balance cost and performance through Customized Storage Solutions, escaping the price competition in the consumer market.
2. Mid-Range Market: Target AI Terminals and Tap into Lightweight Scenarios
With the explosive growth in AI PC shipments (accounting for 31% of the global PC market in 2025), mid-range graphics cards can focus on scenarios such as Edge Computing, intelligent cockpits, and lightweight design. Adopting a combination of DDR5 Memory and optimized GDDR6 Memory, they can take "cost-effective computing power + low-power storage" as the core to adapt to terminal needs such as AI assistants and real-time rendering, opening up a new track integrating consumer electronics and automotive electronics.
3. Domestic Replacement: Seize Supply Chain Opportunities and Break Through Storage Bottlenecks
Against the backdrop of restricted supply of overseas high-end computing power products, domestic graphics card manufacturers should collaborate with local Memory Chip Design enterprises to tackle key components such as Low-Power DRAM and Customized GDDR. Through industrial chain collaboration, they can reduce dependence on imported storage. At the same time, focusing on the mid-range market, they can leverage the advantages of "adapting to local scenarios + stable supply chain" to fill the market gap left by the contraction of international manufacturers.
The Industry Trend Is Clear: Storage Determines the Upper Limit, Scenarios Determine the Increment
The future competition of consumer graphics cards is essentially a competition of Memory Chip Adaptability and scenario implementation capabilities. Single-purpose gaming graphics cards that are divorced from the AI computing wave will eventually be eliminated by the market. Only by keeping up with Memory Technology Iteration and binding to incremental scenarios such as AI Terminals, professional content creation, and intelligent automobiles can they break through and gain a foothold in the industry pattern of high storage costs.
This market reshuffle triggered by Storage Chips is both a challenge and an opportunity. Consumer graphics cards must break the path dependence and take "computing power as the core, storage as the wing, and scenarios as the anchor" to occupy a place in the industrial restructuring of the AI era.
