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市場調查報告書
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2100781

人工智慧記憶體需求激增：HBM記憶體和GPU記憶體之間的頻寬轉換

AI Memory Demand Surge: HBM Memory and GPU Memory Bandwidth Transformation
出版日期: | 出版商: TrendForce | 英文 12 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

本報告首先說明了HBM在LLM和AIGC應用演進中扮演的關鍵角色。其次，報告探討了HBM升級藍圖，並分析了異質記憶體整合策略如何擴展容量、提升頻寬，以滿足未來AIGC的需求。

主要亮點

  • 本節說明了HBM 在 LLM 和 AIGC 工作負載中的關鍵角色。
  • 本文概述了 HBM 升級藍圖和未來發展方向。
  • 我們將檢驗異質記憶體整合以提高容量和頻寬。
  • 我們將這些策略與 AIGC 的新興性能要求聯繫起來。

目錄

  • 1. LLM 的快速發展和 AIGC 應用的爆炸性成長正在推動對 HBM 的無限需求。
  • 2. 重點透過以 HBM 為中心的異質記憶體整合來提高容量和頻寬。
  • 3. TRI的觀點
簡介目錄
Product Code: TRi-190

This report first explains why HBM plays a key role in the evolution of LLM and AIGC applications. It then examines the HBM upgrade roadmap and explores how heterogeneous memory integration strategies can expand capacity and increase bandwidth to meet future AIGC demand.

Key Highlights

  • Explains the pivotal role of HBM in LLM and AIGC workloads.
  • Reviews the HBM upgrade roadmap and future development.
  • Examines heterogeneous memory integration to boost capacity and bandwidth.
  • Links these strategies to emerging AIGC performance requirements.

Table of Contents

  • 1. Infinite HBM Demand Fueled by Rapid LLM Evolution and Explosive Growth of AIGC Applications
    • Table 1: Token Processing Capacity Across GPT Model Generations
    • Figure 1: Text Generation Workflow and HBM Usage
    • Figure 2: Image Generation Workflow and HBM Usage
    • Figure 3: Video Generation Workflow and HBM Usage
    • Table 2: Estimated HBM Requirements for Three Types of AI Generation Workloads
  • 2. HBM-Centric Heterogeneous Memory Integration to Focus on Capacity and Bandwidth Upgrades
    • Table 3: Theoretical Performance and Specification Parameters of Past HBM
    • Figure 4: Schematics of Integration between HBM4 and LPDDR
    • Figure 5: Integration between HBM and NMC Die
    • Figure 6: Schematics of Integration between HBM5 and SOCAMM
    • Figure 7: Schematics of HBM6 with Dual-Tower Architecture
    • Figure 8: Schematics of Integration between HBM7 and HBF
  • 3. TRI’s View