Cloud Load Balancer, the secret to uptime for AI inference

Key Concepts:

• AI Hypercomputer: A fully integrated supercomputing architecture for AI infrastructure.
• Google Kubernetes Engine (GKE): A managed Kubernetes service for deploying, scaling, and managing containerized applications.
• Cloud Load Balancer: A service for distributing traffic across multiple backend servers.
• Service Extensions: Customizable extensions to Cloud Load Balancer for intelligent routing and prompt processing.
• GPUs and TPUs: Hardware accelerators for AI workloads (Graphics Processing Units and Tensor Processing Units).
• Parallelstore and GCS FUSE: Storage solutions for AI models and data.
• JAX and PyTorch: Popular machine learning frameworks.
• JetStream: Inference-accelerating engine.
• Custom Metrics: User-defined metrics used for intelligent traffic distribution.
• Availability (Nines): A measure of system uptime, expressed as a percentage (e.g., 99.9% availability).
• Multimodal Architectures: AI systems that process multiple types of data (e.g., text, images, audio).

1. The Challenge of AI Application Reliability

• Applications evolve from MVPs to complex systems with grafted features and integrations, leading to reliability issues.
• Traditional infrastructure management struggles with the dynamic nature of AI applications, which require seamless scaling, fault tolerance, and intelligent traffic management.

2. AI Hypercomputer: An End-to-End Solution

• AI Hypercomputer is presented as a solution to build reliability into AI infrastructure from the start.
• It's a fully integrated supercomputing architecture optimized for performance, speed, and cost-efficiency.
• It addresses the challenges of serving AI workloads reliably by offering scalability and fault tolerance.

3. Architecture and Components

• The architecture is based on AI Hypercomputer running on Google Kubernetes Engine (GKE).
• Users can choose performance-optimized hardware: GPUs or TPUs, Parallelstore or GCS FUSE.
• Users can choose their framework: JAX or PyTorch.
• Example configuration: TPUs with the inference-accelerating JetStream engine using JAX and GCS FUSE with SSDs.
• Cloud Load Balancer with custom metrics and service extensions is layered on top.

4. GKE and Cloud Load Balancer Integration

• GKE automates the deployment, scaling, and management of containerized AI applications with a 99.9% pod-level uptime SLA.
• Cloud Load Balancer with custom metrics ensures high availability and intelligent traffic distribution.
• Cloud Load Balancing supports over 1 million queries per second.

5. Addressing New Challenges with Service Extensions

• AI applications introduce new reliability challenges related to prompt size, multimodality, safety, and accuracy.
• Service extensions enable intelligent routing of prompts anywhere in multimodal architectures.
• This allows connecting the right models to the right prompts and protecting backends from unsafe prompts.

6. The Importance of Availability (Nines)

• The number of nines in availability (99%, 99.9%, 99.99%) is crucial for application reliability.
• Solutions like AI Hypercomputer can help achieve high levels of availability.

7. Call to Action

• Viewers are encouraged to check out the blog for more information, access the reference architecture, and deployment guides.

8. Synthesis/Conclusion

The video highlights the challenges of maintaining reliability in AI applications and introduces AI Hypercomputer as a comprehensive solution. By leveraging GKE, Cloud Load Balancer with service extensions, and optimized hardware/software choices, organizations can build highly available and scalable AI infrastructure. The emphasis on intelligent prompt routing and safety measures underscores the unique reliability considerations for AI workloads. The video concludes with a call to action, encouraging viewers to explore available resources for implementing AI Hypercomputer.