TPU vs. GPU：2025 年 AI 加速器市場驚人前景

TPU vs GPU Comparison: 2025 AI Accelerator Market Shockwave Outlook

出版日期: 2025年12月16日 | 出版商:

TrendForce | 英文 25 Pages | 商品交期: 最快1-2個工作天內

價格

簡介目錄

全球 AI 硬體市場持續擴張，這主要得益於資本投資的增加以及雲端服務供應商 (CSP) 對先進運算資源的加速採購。為此，各公司紛紛與Google合作推出 TPU（張量處理單元），同時 TPU 技術也不斷發展，透過增強架構和供應鏈策略來支援下一代 AI 模型。 AI 應用正日益被視為高性能轉型的重要推動因素。

主要亮點

全球 AI 硬體市場持續擴張，這主要得益於資本投資的增加以及雲端服務供應商 (CSP) 對先進運算資源的加速採購。
Google的 Gemini 三部曲打破了 TPU 不適合訓練的長期迷思，證明了 TPU 能夠處理訓練和推理的核心工作負載。
這項成功源自於TPU硬體和Gemini演算法的閉迴路協同設計，從而建立了難以複製的競爭優勢。
隨著人工智慧產業從訓練轉向推理，而ASIC晶片在大規模推理中更受青睞，這使得基於TPU的平台在人工智慧開發者和供應商中越來越受歡迎。
TPU和GPU是互補而非替代，由於雲端服務供應商（CSP）的投資和人工智慧硬體採購的加速，TPU的出貨量正在蓬勃發展。

The global AI hardware market continues to expand, driven by increasing CSP capital expenditures and accelerated procurement of advanced computing resources. In response, companies are partnering with Google to adopt TPUs, while TPU technology evolves with enhanced architectures and supply chain strategies to support next-generation AI models. AI applications are increasingly recognized as a key enabler of high-performance transformation.

Key Highlights

The global AI hardware market continues to expand, driven by increasing CSP capital expenditures and accelerated procurement of advanced computing resources.
Google's Gemini trilogy overturns the long-held belief that TPUs are unsuitable for training, proving their capability for both training and inference core workloads.
Success stems from co-designing TPU hardware and Gemini algorithms in a closed feedback loop, creating a hard-to-replicate competitive moat.
This sparks interest among AI developers and vendors in TPU-based platforms, with AI industry shifting from training to inference, favoring ASICs for large-scale inference.
TPUs and GPUs are complementary rather than substitutes, with TPU shipments surging due to CSP investments and AI hardware procurement acceleration.