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. 

Table of Contents

1. Gemini 3.0 Makes Headlines, TPUs Draw Market Attention
2. Training vs. Inference in AI Models: Shifting Priorities and Future Trajectories
3. TPU vs. GPU: A Comparative Overview
4. TPUs Enter a Phase of Explosive Growth
5. Key TPU Supply Chain
6. TRI’s View

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