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AI Hypercomputer is a supercomputing system that is optimized to
support your artificial intelligence (AI) and machine learning (ML) workloads.
It's an integrated system of performance-optimized hardware, open software, ML
frameworks, and flexible consumption models.
AI Hypercomputer uses best practices and systems-level designs to boost
efficiency and productivity across AI pre-training, tuning, and serving.
System architecture
AI Hypercomputer is comprised of the following layers:
Performance-optimized infrastructure: contains accelerators,
networking, and storage resources that provide the computing capabilities
to support your workloads.
Open software: optimized versions of popular machine learning
frameworks such as TensorFlow, PyTorch, and JAX. Google provides
operating systems (OS) that are configured with essential software for
leveraging the compute resources provisioned in your clusters.
To deploy and manage a large number of accelerators as a single unit, you
can also use Cluster Director for Google Kubernetes Engine, or Cluster Director
for Slurm, or directly through Compute Engine APIs.
Consumption options: multiple options to provision clusters that
optimize costs and hardware availability based on your specific needs and
workload patterns.
Benefits
AI Hypercomputer has the following benefits:
High performance and goodput: Goodput metrics measure ML Productivity.
AI Hypercomputer optimizes the scheduling, runtime, and
orchestration layers.
Get up and running quickly: AI Hypercomputer provides tools and
blueprints that
let you reliably and repeatedly deploy large numbers of accelerator-optimized
resources that are configured to support your most demanding AI and ML
workloads.
Use cases
AI Hypercomputer was designed to meet the needs of the following use cases:
Use case
Example workloads
Large-scale AI and ML workloads
Generative AI distributed training
Generative AI inference
Fraud detection
Recommendation models
High performance computing (HPC)
Complex simulations
Drug discovery, protein folding, and genomic analysis
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