TPU vs. GPU：2025 年 AI 加速器市场惊人前景

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个工作天内

价格

简介目录

全球 AI 硬体市场持续扩张，这主要得益于资本投资的增加以及云端服务供应商 (CSP) 对先进运算资源的加速采购。为此，各公司纷纷与Google合作推出 TPU（张量处理单元），同时 TPU 技术也不断发展，透过增强架构和供应链策略来支援下一代 AI 模型。 AI 应用正日益被视为高性能转型的重要推动因素。

主要亮点

全球 AI 硬体市场持续扩张，这主要得益于资本投资的增加以及云端服务供应商 (CSP) 对先进运算资源的加速采购。
Google的 Gemini 三部曲打破了 TPU 不适合训练的长期迷思，证明了 TPU 能够处理训练和推理的核心工作负载。
这项成功源自于TPU硬体和Gemini演算法的闭迴路协同设计，从而建立了难以复製的竞争优势。
随着人工智慧产业从训练转向推理，而ASIC晶片在大规模推理中更受青睐，这使得基于TPU的平台在人工智慧开发者和供应商中越来越受欢迎。
TPU和GPU是互补而非替代，由于云端服务供应商（CSP）的投资和人工智慧硬体采购的加速，TPU的出货量正在蓬勃发展。

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.