As artificial intelligence continues to revolutionize the tech world, the infrastructure behind it is becoming just as important as the software itself. One of the biggest shifts in this space is Google’s move to develop custom cloud chips—powerful, efficient processors built in-house to fuel AI models like Gemini and even power services for major clients like Apple. These chips represent the next frontier of performance, efficiency, and competitive advantage in the cloud computing race.
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The Era of Custom Cloud Chips
Traditionally, companies relied on third-party processors from giants like Intel, NVIDIA, and AMD. But in the age of AI, hyperscale cloud providers like Google, Amazon, and Microsoft are realizing that designing their own chips gives them greater control over performance, energy usage, and cost.
For Google, this journey began nearly a decade ago—and today, its custom silicon is central to how it runs some of the world’s largest workloads, including:
- Training and inference of Gemini AI models
- Serving cloud-based AI tools for external clients
- Running parts of Apple’s AI services on Google Cloud
Meet Google’s Custom Chips: TPU and Axion
Tensor Processing Unit (TPU)
Google’s most well-known chip line is the Tensor Processing Unit (TPU), first introduced in 2016. Built specifically for machine learning workloads, TPUs are optimized for the vast matrix multiplications and operations required in deep learning.
Key milestones:
- TPU v4: Used in 2023–2024 to train large models like Gemini 1 and Gemini 1.5.
- TPU v5e and v5p (2024–2025): High-efficiency and high-performance versions designed for both training and inference at scale.
TPUs are tightly integrated into Google’s cloud infrastructure and are not sold like traditional chips. Instead, they are accessed through Google Cloud AI services, enabling clients (like Apple) to rent massive compute clusters for tasks like voice recognition, image analysis, and AI chatbot functions.
Axion: Google’s Arm-Based CPU
In early 2024, Google unveiled Axion, its first custom Arm-based general-purpose CPU. While TPUs handle AI-specific tasks, Axion is designed to replace x86 CPUs in general cloud workloads—making everything from web hosting to AI preprocessing more efficient.
With Axion, Google joins Amazon (Graviton chips) in pushing a broader vertical integration strategy, giving them the ability to fine-tune both hardware and software to their needs.
Apple’s AI Services on Google Cloud
In a surprising but strategic move, Apple reportedly began running some of its AI services on Google Cloud in 2024–2025, particularly those related to:
- Siri improvements
- Device-cloud hybrid AI processing
- Apple Intelligence services for iPhone, iPad, and Mac
Because of Apple’s strong focus on user privacy and performance, Google’s custom TPUs and AI-optimized infrastructure likely offered the right blend of compute power and secure isolation to meet Apple’s strict standards.
While Apple designs its own on-device chips (like the M-series and A-series), training and processing large-scale AI models—especially in real-time—often requires cloud-level infrastructure, and Google’s AI-optimized cloud is among the most advanced available.
Why Custom Chips Matter in the AI Race
Custom silicon allows Google to:
- Optimize cost and power usage: By reducing reliance on NVIDIA or AMD, Google can cut infrastructure costs and power bills—especially important as AI workloads soar.
- Control the full stack: Integration between hardware, software, and data centers allows for better performance tuning and scalability.
- Secure a strategic edge: In an AI-first world, owning the infrastructure layer can be as powerful as owning the algorithm itself.
These custom chips are also a response to the global chip supply crunch and geopolitical risks, giving Google more independence and long-term stability.
What's Next?
Google is continuing to invest in next-gen TPUs and AI supercomputers, building clusters with tens of thousands of interconnected chips, capable of training ever-larger models like Gemini 2 and future multimodal AI systems.
There are also rumors of a Google-designed AI inference chip for mobile devices, potentially bringing its silicon expertise closer to end users—especially as Android and Pixel devices integrate Gemini more deeply.
By building its own cloud chips like TPUs and Axion, Google has positioned itself as both a platform and infrastructure leader in the AI era. These chips now power everything from Gemini to Apple AI, making Google not just a cloud provider but a foundational player in the next phase of intelligent computing.
In the ongoing AI arms race, custom chips are the new battlefield—and Google is already a dominant force.