Integrated supercomputing architecture

AI Hypercomputer

AI optimized hardware, software, and consumption, combined to improve productivity and efficiency.

Overview

Performance-optimized hardware

Our performance optimized infrastructure including Google Cloud TPU, Google Cloud GPU, Google Cloud Storage and the underlying Jupiter network consistently provides fastest time to train for large scale state of the art models due to the strong scaling characteristics of the architecture which leads to the best price/performance for serving large models.

Open software

Our architecture is optimized to support the most common tools and libraries such as Tensorflow, Pytorch and JAX. Plus it allows customers to take advantage of technologies such as Cloud TPU Multislice and Multihost configurations and managed services like Google Kubernetes Engine. This allows customers to deliver turnkey deployment for common workloads like the NVIDIA NeMO framework orchestrated by SLURM.

Flexible consumption

Our flexible consumption models allow customers to choose fixed costs with committed use discounts or dynamic on-demand models to meet their business needs. Dynamic Workload Scheduler helps customers get the capacity they need without over allocating so they are only paying for what they need. Plus, Google Cloud's cost optimization tools help automate resource utilization to reduce manual tasks for engineers.

How It Works

Google is a leader in artificial intelligence with the invention of technologies like TensorFlow. Did you know you can leverage Google’s technology for your own projects? Learn about Google's history of innovation in AI infrastructure and how you can leverage it for your workloads.

Google Cloud AI Hypercomputer architecture diagram alongside the Google Cloud product manager Chelsie's photo

Common Uses

Run large-scale AI training

Powerful, scalable, and efficient AI training

The AI Hypercomputer architecture offers optionality to use the underlying infrastructure that best scales to meet your training needs.

How to define a storage infrastructure for AI workloads
Three Charts Describing AI Growth Factors

Powerful, scalable, and efficient AI training

The AI Hypercomputer architecture offers optionality to use the underlying infrastructure that best scales to meet your training needs.

How to define a storage infrastructure for AI workloads
Three Charts Describing AI Growth Factors

Character AI leverages Google Cloud to scale up

"We need GPUs to generate responses to users' messages. And as we get more users on our platform, we need more GPUs to serve them. So on Google Cloud, we can experiment to find what is the right platform for a particular workload. It's great to have that flexibility to choose which solutions are most valuable." Myle Ott, Founding Engineer, Character.AI

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Deliver AI powered applications

Leverage open frameworks to deliver AI powered experiences

Google cloud is committed to ensuring open frameworks work well within the AI Hypercomputer architecture.

RAG in production faster with Ray, LangChain, and HuggingFace
High Level RAG Architecture

Leverage open frameworks to deliver AI powered experiences

Google cloud is committed to ensuring open frameworks work well within the AI Hypercomputer architecture.

RAG in production faster with Ray, LangChain, and HuggingFace
High Level RAG Architecture

Leverage open frameworks to deliver AI powered experiences

Google Cloud's open software ecosystem allows you to build applications with the tools and frameworks you are most comfortable with, while taking advantage of the price-performance benefits of the AI Hypercomputer architecture.

Google Cloud AI tools and frameworks

Priceline: Helping travelers curate unique experiences

"Working with Google Cloud to incorporate generative AI allows us to create a bespoke travel concierge within our chatbot. We want our customers to go beyond planning a trip and help them curate their unique travel experience." Martin Brodbeck, CTO, Priceline

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Cost efficiently serve models at scale

Maximize price/performance for serving AI at scale

Google Cloud provides industry leading price/performance for serving AI models with accelerator optionality to address any workload's needs.

Minimized inference latency with custom AI-aware load balancing
Load Balancing Based on Queue Depth Architecture Diagram

Maximize price/performance for serving AI at scale

Google Cloud provides industry leading price/performance for serving AI models with accelerator optionality to address any workload's needs.

Minimized inference latency with custom AI-aware load balancing
Load Balancing Based on Queue Depth Architecture Diagram

Maximize price/performance for serving AI at scale

Cloud TPU v5e and G2 VM Instances delivering NVIDIA L4 GPUs enable high-performance and cost-effective inference for a wide range of AI workloads, including the latest LLMs and Gen AI models. Both offer significant price performance improvements over previous models and Google Cloud's AI Hypercomputer architecture enables customers to scale their deployments to industry leading levels.

Relative performance per dollar: Cloud TPU v4 and v5e

Assembly AI leverage Google Cloud for cost efficiency

"Our experimental results show that Cloud TPU v5e is the most cost-efficient accelerator on which to run large-scale inference for our model. It delivers 2.7x greater performance per dollar than G2 and 4.2x greater performance per dollar than A2 instances." Domenic Donato,

VP of Technology, AssemblyAI


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Open source models on Google Cloud

Serve a model with GKE on a single GPU

Train common models with GPUs

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