High Bandwidth Memory (HBM) Market Forecast Driven by AI, Networking, and Memory Performance Demands | Valuates Reports

High Bandwidth Memory (HBM) Market Size
The global market for High Bandwidth Memory (HBM) was valued at US$ 3803 million in the year 2024 and is projected to reach a revised size of US$ 138290 million by 2031, growing at a CAGR of 68.1% during the forecast period.

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The High Bandwidth Memory (HBM) market is experiencing robust market growth, propelled by escalating demand for ultra-fast memory solutions across AI accelerators, data centers, and networking infrastructure. As data throughput and memory latency become critical for complex workloads, the market size continues to expand in response to evolving performance benchmarks.

By memory type, HBM3 and HBM3E dominate current market share, offering higher data rates and enhanced energy efficiency tailored for cutting-edge GPUs, AI systems, and high-performance computing. These are closely followed by HBM2E, valued for its mature integration in existing platforms. HBM2 remains essential in legacy and mid-tier applications, while emerging others such as HBM4 developments are beginning to gain attention with early-stage adoption and innovation trends.

In terms of application, servers, including hyperscale and AI training systems, represent the largest segment in market size, due to their reliance on high-throughput memory bandwidth. Networking hardware-such as data center switches and network accelerators-also command significant share, fueled by real-time traffic processing requirements. The consumer segment-encompassing high-end graphics cards and AI-enabled PCs-contributes notably, supported by demand-driven performance gains. Other applications, including edge AI nodes, automotive compute, and scientific instrumentation, show promising growth as HBM becomes the preferred choice for latency-sensitive data processing.

Overall, the HBM market forecast indicates sustained market growth, driven by continued HBM architecture advancements, increased deployment in AI and networking platforms, and expansion of memory-intensive applications across computing landscapes.

by Type

• HBM2
• HBM2E
• HBM3
• HBM3E
• Others

by Application

• Servers
• Networking
• Consumer
• Others

By Company

SK Hynix, Samsung, Micron

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