Google is building a hardware and software ecosystem that can deploy its custom TPUs.

Google is fully committed to building a comprehensive technology ecosystem encompassing hardware, software, frameworks, and cloud services, and is continuously expanding its influence in the field of artificial intelligence computing power through its independently developed custom Tensor Processing Units (TPUs).

This systematic layout not only covers chip-level architectural innovation but also deeply integrates machine learning frameworks such as TensorFlow and JAX, as well as cloud platform services optimized for large-scale AI workloads, forming a complete closed loop from training to inference, and from model development to production deployment.

At the hardware level, Google's TPUs have undergone multiple architectural iterations, and their advantages in performance-to-power ratio, model parallelism capabilities, and collaboration with large-scale clusters are becoming increasingly prominent.

Through large-scale deployment in core businesses such as Google Cloud, search, translation, and recommendation systems, TPUs have not only significantly improved the operational efficiency of its internal AI businesses but are also gradually being offered externally as a computing power solution that can be widely used in the industry.

Facing the strong position established by Nvidia with its GPU CUDA ecosystem, Google's approach is not simply hardware replacement, but rather centered on the core concept of "software-hardware collaboration and system optimization," building a more controllable and independent AI infrastructure.

This end-to-end integration capability gives Google greater flexibility, cost control, and technological influence in addressing future technological trends requiring ultra-large-scale computing power, such as large models and generative AI.

As the competition in artificial intelligence enters a new stage based on computing power and protected by ecosystems, Google, with its continuous investment in TPUs, deep optimization of software and hardware integration, and extensive reach in the global cloud computing market, is gradually challenging Nvidia's monopoly in the field of AI acceleration chips.

Although Nvidia currently still holds a dominant market position, the complete technology stack built by Google undoubtedly constitutes the most systematic and sustainable long-term challenge, and also introduces significant variables into the future evolution of the global AI industry structure.