為什麼存儲芯片價格上漲dram nand價格上漲

理解AI時代的記憶超級周期

As the industry moves into 2026, the global technology sector is entering what many analysts describe as a memory supercycle. Unlike past pricing cycles driven mainly by short-term supply and demand fluctuations, this phase reflects deeper structural changes in how memory is produced, allocated, and consumed.

從pc和智能手機到AI服務器和智能終端,DRAM和NAND價格的上漲正在影響整個硬件生態系統,包括教育和企業協作中使用的AI交互式白板。

Many buyers are beginning to ask:

Why are hardware prices increasing even with similar specifications?

為什麼有些交付時間越來越長?

How do memory prices affect AI interactive whiteboards specifically?

To answer these questions, it is important to understand how AI is reshaping memory demand worldwide.

記憶成為AI時代的核心資源

In traditional devices, memory and storage were supportive components. In AI-powered systems, they are foundational.

AI workloads rely heavily on rapid data access, caching, and real-time computation. This requires:

• Larger memory capacity
• Higher bandwidth
• Faster storage read/write speeds
• 穩定的數據緩存性能

簡單來說: AI consumes memory as aggressively as it consumes computing power.

A device running advanced AI features may require several times more memory resources than a standard smart device.

This shift is fundamentally changing global memory demand patterns.

Three Key Drivers Behind Rising DRAM and NAND Prices

1) AI Capacity “Siphoning Effect”

The rapid expansion of generative AI and AI agents has made High Bandwidth Memory (HBM) one of the most in-demand semiconductor products.

三星、SK海力士和美光等主要製造商正在將更多的產能分配給:

• AI training servers
• 超大規模數據中心
• High-performance computing clusters

These segments offer higher margins and long-term contracts, making them a priority.

As a result, supply available for mainstream DRAM and NAND applications is indirectly reduced, tightening the market.

2) Structural Production Constraints

先進的存儲器製造需要超淨的設施、尖端的光刻技術和大量的資本投資。

擴大產能並不快。 即使今天開始新的投資,有意義的供應增加也可能需要一到兩年或更長時間。

This creates a structural constraint rather than a temporary shortage.

3) Low Inventory and Forward Purchasing

Industry-wide inventory levels have normalized after earlier corrections. With expectations of continued price increases, many OEMs are securing supply early.

這種遠期購買行為進一步推高了現貨和合約價格。

Why AI Interactive Whiteboards Are Directly Affected

Some assume only servers require high-performance memory. In reality, modern AI interactive whiteboards are also edge AI computing devices.

They perform local AI processing for tasks such as:

• Real-time speech recognition
• Multi-language translation
• AI-generated meeting summaries
• Intelligent handwriting recognition
• Image understanding and content analysis
• Multi-application multitasking

All of these depend on:

• High-capacity DRAM
• High-speed NAND storage
• Reliable data caching

In a premium AI interactive whiteboard, memory and storage can account for 15–20% of the total bill of materials. When memory prices rise, hardware costs naturally follow.

Why Some Products Are Quietly Downgrading Specifications

Under cost pressure, some manufacturers may reduce configurations, for example:

• Lower RAM capacity
• Older generation storage
• Slower read/write solutions

While this may control short-term pricing, it can lead to:

• Slower AI response
• Lag during multitasking
• Reduced system stability
• Shorter product lifespan

For education and enterprise environments, long-term reliability and smooth performance are far more valuable than minimal upfront savings.

How Qtenboard Responds to the Memory Supercycle

In a changing market, Qtenboard focuses on long-term value rather than short-term cost competition.

Proactive Supply Chain Planning

Through long-term partnerships with key suppliers, Qtenboard secures critical memory components in advance, helping stabilize supply during volatile periods.

Commitment to Full AI Performance

Qtenboard does not reduce essential memory specifications in its AI interactive whiteboards simply to lower costs.

True AI capability requires strong hardware foundations. Compromising memory means compromising user experience.

Software-Level Optimization

Qtenboard continuously optimizes system architecture and AI resource management to improve memory utilization efficiency.

This hardware–software synergy allows customers to gain more value even in a rising-cost environment.

Practical Advice for Buyers

In the current landscape, strategic planning matters more than waiting for prices to drop.

For schools and enterprises planning AI interactive whiteboard deployments:

• Plan projects earlier
• Work with stable suppliers
• Evaluate long-term value, not only initial price
• Consider lifecycle performance and reliability

These factors often determine total cost of ownership more than the purchase price alone.

Conclusion: A Structural Realignment, Not a Temporary Spike

The rise in DRAM and NAND prices reflects a broader realignment of semiconductor priorities in the AI era.

Memory is evolving from a background component into a strategic resource. Hardware cost structures are being redefined accordingly.

For buyers, the key is not concern, but awareness and informed decision-making.

Qtenboard remains committed to delivering AI interactive whiteboards that combine intelligent capability, stable performance, and long-term value — designed for the future of collaboration and education.