Co-design is the architecture of intelligence
To deliver the deterministic efficiency and extreme scale required to solve the world’s most complex AI challenges, we co-evolve the entire compute stack as a single, cohesive unit.
Pioneering unified co-design
Because Google owns the end-to-end AI stack, we’re uniquely able to design frontier models and infrastructure in tandem as an inseparable system—from data centers to hardware and surrounding software. This results in an industry-leading foundation that enables AI-powered breakthroughs for Google and our customers.
The ‘chip to serving’ operational engine
We view warehouse-scale computing through the framework of vertically-integrated systems design, optimizing the entire lifecycle from ‘chip to serving’—including supply chain, engineering, physical infrastructure, and operations—as a cohesive unit rather than separate silos, enabling strategic trade-offs to manage supply chain risks, velocity, reliability, maintenance, and physical automation.
Hyper-specialization to 100x system efficiency
Co-design across multiple layers—silicon and interconnects, specialized networking, and bespoke data centers—dramatically optimizes performance, power, and cost for AI workloads. For example, custom chips with purpose-built interconnects can synchronously coordinate hundreds of thousands of nodes within microseconds. Custom-engineered high-speed network fabrics are tailored specifically for intense, massive-scale AI traffic. And custom enclosures, racks, power delivery, and advanced cooling systems are optimized purely for end-to-end system performance.
Solving the AI power paradox
Adding more racks isn't a sustainable scaling strategy. We focus on architectural efficiency, which enables us to extract more intelligence per watt through custom silicon and liquid cooling—decoupling the growth of AI from the growth of carbon and energy consumption.
We build the serving infrastructure as the model is being born
The Google DeepMind feedback loop
The frontier researchers building new capabilities for models like Gemini directly inform the engineering of the computing infrastructure. This ensures TPUs are optimized for frontier AI and Google’s frontier models are trained on the very hardware they were designed for, resulting in “deterministic efficiency”—predictable, high-speed training cycles.
Pioneering algorithmic co-design
Optimize agent communication topology
Treats agent communication and intelligence generation as a single fabric, increasing efficiency without brute-force hardware scaling.
Adapt Transformers via soft token merging
Weaves software intelligence with hardware limits by merging redundant tokens at the algorithmic layer to use less on-chip memory.
Bypass memory limits with pure convolution
Co-designs vision models for hardware realities, using pure convolutions to bypass the quadratic memory bottlenecks of attention.
Accelerating research to production
Google’s planetary-scale ecosystem acts as an early production proving ground for training and large-scale inference scaling. We leverage its immense scale to prove and refine our custom hardware, ensuring breakthroughs achieved with research are immediately ready for the rigors of production.
“By co-designing the entire stack—from the physics of the data center to the frontier of the model—we ensure that infrastructure is not a limit to our imagination.”
Unlocking the engine of innovation
Increase efficiency
Run frontier-scale models without the unpredictable costs of generic cloud infrastructure. Google’s full-stack approach drives higher utilization and deterministic performance.
Eliminate the integration tax
Remove the friction and performance loss found in fragmented stacks when using hardware and software designed as one inseparable system.
Future-proof your AI roadmap
Stay one step ahead of the most demanding research breakthroughs. We partner with Google DeepMind to use AI to design the very infrastructure that will train the next generation of models.
Enabling breakthroughs across Google’s most iconic products
Co-designing the Mixture-of-Experts architecture of Gemini models with TPUs enables breaking down multi-step problems and weighing trade-offs with advanced reasoning.
Workspace and Cloud have moved to agentic systems like Google Antigravity, which collaborate interactively to automate entire workflows.
Search has evolved into a dynamic reasoning engine, where Gemini models synthesize complex answers and orchestrate tasks like booking travel or research.
YouTube uses Gemini’s multimodal intelligence to enable real-time video understanding.
Enabling cutting-edge, compute-intensive research
Accelerating scientific discovery with Co-Scientist
A multi-agent AI system designed to act as a collaborative partner for generating and refining breakthrough scientific hypotheses.
Supercharging LLMs for formal mathematics
LEAP is a state-of-the-art agentic framework that bridges the gap between informal mathematical reasoning and formal, machine-verifiable proof generation in the Lean language.