AI Hypercomputer overview (original) (raw)
AI Hypercomputer overview
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.
The AI Hypercomputer system incorporates best practices and systems-level design 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 use Cluster Director, Google Kubernetes Engine, or Slurm. Alternatively, you can manually deploy your resources by using the 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:goodputmetrics measure ML productivity. AI Hypercomputer optimizes the scheduling, runtime, and orchestration layers.
- Get up and running quickly: AI Hypercomputer provides tools, such as Cluster Director andblueprints, 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.
- Storage layer that's optimized for performance: use high-performance storage services, such as Cloud Storage and Google Cloud Managed Lustre, to provide scalable, high throughput, low latency storage for 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 Risk analysis and quantitative trading |
What's next
- Learn about the performance-optimized infrastructure of AI Hypercomputer:
- Review consumption models.
- Learn about cluster management.
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Last updated 2026-06-15 UTC.