Ready to get started with AI Hypercomputers? This course makes it easy! We'll cover the basics of what they are and how they help AI with AI workloads. You'll learn about the different components inside a hypercomputer, like GPUs, TPUs, and CPUs, and discover how to pick the right deployment approach for your needs.
AI Infrastructure: Introduction to AI Hypercomputer
AI Infrastructure: Introduction to AI Hypercomputer
This course is part of Google Cloud AI Infrastructure Specialization
Instructor: Google Cloud Training
Access provided by The Adult Learning Department at DCPL
Gain insight into a topic and learn the fundamentals.
4.1
(14 reviews)
Intermediate level
Some related experience required
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Define the value and architecture of the AI Hypercomputer
Identify common use cases for using AI Hypercomputer
Explain how different types of accelerators (GPUs, TPUs, CPUs) contribute to the acceleration of AI training and inference.
Differentiate between various deployment options and choose the options that best suits your requirements.
Skills you'll gain
Details to know
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Assessments
1 assignment
Taught in English
Recently updated!
August 2025
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This course is part of the Google Cloud AI Infrastructure Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 2 modules in this course
In this course, you'll gain a deeper understanding of how to effectively utilize Google Cloud GPUs for accelerating AI training and inference, including selecting appropriate options and optimizing performance.
What's included
1 assignment7 plugins
Student PDF links to all modules
What's included
1 reading
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