Source URL: https://cloud.google.com/blog/topics/developers-practitioners/supercharge-your-ai-gke-inference-reference-architecture-your-blueprint-for-production-ready-inference/
Source: Cloud Blog
Title: Supercharge your AI: GKE inference reference architecture, your blueprint for production-ready inference
Feedly Summary: The age of AI is here, and organizations everywhere are racing to deploy powerful models to drive innovation, enhance products, and create entirely new user experiences. But moving from a trained model in a lab to a scalable, cost-effective, and production-grade inference service is a significant engineering challenge. It requires deep expertise in infrastructure, networking, security, and all of the Ops (MLOps, LLMOps, DevOps, etc.).
Today, we’re making it dramatically simpler. We’re excited to announce the GKE inference reference architecture: a comprehensive, production-ready blueprint for deploying your inference workloads on Google Kubernetes Engine (GKE).
This isn’t just another guide; it’s an actionable, automated, and opinionated framework designed to give you the best of GKE for inference, right out of the box.
Start with a strong foundation: The GKE base platform
Before you can run, you need a solid place to stand. This reference architecture is built on the GKE base platform. Think of this as the core, foundational layer that provides a streamlined and secure setup for any accelerated workload on GKE.
Built on infrastructure-as-code (IaC) principles using Terraform, the base platform establishes a robust foundation with the following:
Automated, repeatable deployments: Define your entire infrastructure as code for consistency and version control.
Built-in scalability and high availability: Get a configuration that inherently supports autoscaling and is resilient to failures.
Security best practices: Implement critical security measures like private clusters, Shielded GKE Nodes, and secure artifact management from the start.
Integrated observability: Seamlessly connect to Google Cloud Observability for deep visibility into your infrastructure and applications.
Starting with this standardized base ensures you’re building on a secure, scalable, and manageable footing, accelerating your path to production.
Why the inference-optimized platform?
The base platform provides the foundation, and the GKE inference reference architecture is the specialized, high-performance engine that’s built on top of it. It’s an extension that’s tailored specifically to solve the unique challenges of serving machine learning models.
Here’s why you should start with our accelerated platform for your AI inference workloads:
1. Optimized for performance and cost
Inference is a balancing act between latency, throughput, and cost. This architecture is fine-tuned to master that balance.
Intelligent accelerator use: It streamlines the use of GPUs and TPUs, so you can use custom compute classes to ensure that your pods land on the exact hardware they need. With node auto-provisioning (NAP), the cluster automatically provisions the right resources, when you need them.
Smarter scaling: Go beyond basic CPU and memory scaling. We integrate a custom metrics adapter that allows the Horizontal Pod Autoscaler (HPA) to scale your models. Scaling is based on real-world inference metrics like queries per second (QPS) or latency, ensuring you only pay for what you use.
Faster model loading: Large models mean large container images. We leverage the Container File System API and Image streaming in GKE along with Cloud Storage FUSE to dramatically reduce pod startup times. Your containers can start while the model data streams in the background, minimizing cold-start latency.
2. Built to scale any inference pattern
Whether you’re doing real-time fraud detection, batch processing analytics, or serving a massive frontier model, this architecture is designed to handle it. It provides a framework for the following:
Real-time (online) inference: Prioritizes low-latency responses for interactive applications.
Batch (offline) inference: Efficiently processes large volumes of data for non-time-sensitive tasks.
Streaming inference: Continuously processes data as it arrives from sources like Pub/Sub.
The architecture leverages GKE features like the cluster autoscaler and the Gateway API for advanced, flexible, and powerful traffic management that can handle massive request volumes gracefully.
3. Simplified operations for complex models
We’ve baked in features to abstract away the complexity of serving modern AI models, especially LLMs. The architecture includes guidance and integrations for advanced model optimization techniques such as quantization (INT8/INT4), tensor and pipeline parallelism, and KV Cache optimizations like Paged and Flash Attention.
Furthermore, with GKE in Autopilot mode, you can offload node management entirely to Google, so you can focus on your models, not your infrastructure.
Get started today!
Ready to build your inference platform on GKE? The GKE inference reference architecture is available today in the Google Cloud Accelerated Platforms GitHub repository. The repository contains everything that you need to get started, including the Terraform code, documentation, and example use cases.
We’ve included examples for deploying popular workloads like ComfyUI and a general-purpose online inference with GPUs and TPUs to help you get started quickly.
By combining the rock-solid foundation of the GKE base platform with the performance and operational enhancements of the inference reference architecture, you can deploy your AI workloads with confidence, speed, and efficiency. Stop reinventing the wheel and start building the future on GKE.
The future of AI on GKE
The GKE inference reference architecture is more than just a collection of tools, it’s a reflection of Google’s commitment to making GKE the best platform for running your inference workloads. By providing a clear, opinionated, and extensible architecture, we are empowering you to accelerate your AI journey and bring your innovative ideas to life.
We’re excited to see what you’ll build with the GKE inference reference architecture. Your feedback is welcome! Please share your thoughts in the GitHub repository.
AI Summary and Description: Yes
**Summary:** The provided text discusses the introduction of the GKE inference reference architecture, which simplifies the deployment of AI inference workloads on Google Kubernetes Engine (GKE). It emphasizes the benefits of infrastructure-as-code principles, security best practices, and operational efficiencies tailored for machine learning models, thereby appealing to professionals in AI, cloud computing, and infrastructure security.
**Detailed Description:**
The text highlights a significant development in the deployment of AI inference workloads through Google’s GKE inference reference architecture. It provides a structured and optimized foundation for organizations looking to enhance their services using AI technology.
– **Key Features of the GKE Inference Reference Architecture:**
– **Core Foundation on GKE Base Platform:**
– Establishes a solid and secure base for deploying workloads.
– Relies on infrastructure-as-code (IaC) principles using Terraform, ensuring consistency through automated, repeatable deployments.
– Offers built-in features for scalability and high availability.
– Incorporates essential security measures:
– Private clusters
– Shielded GKE Nodes
– Secure artifact management
– Provides integrated observability for better monitoring of infrastructure and applications.
– **Optimization for Inference Workloads:**
– Focuses on balancing latency, throughput, and cost for efficient performance.
– Features intelligent accelerator use which allows for optimized deployment on GPUs and TPUs.
– Custom metrics integration to refine scaling methods based on actual performance metrics like queries per second (QPS).
– **Support for Various Inference Patterns:**
– Designed to manage different inference patterns:
– Real-time, low-latency inference for interactive applications.
– Batch processing for non-time-sensitive tasks.
– Streaming inference for continuous data processing.
– **Simplified Management for Advanced Models:**
– Abstracts the complexities involved in serving modern AI models, particularly large language models (LLMs).
– Includes guidance on model optimization techniques such as quantization and parallelism.
– GKE’s Autopilot mode allows users to offload node management, focusing more on model development rather than underlying infrastructure.
– **Resources for Implementation:**
– The architecture is accessible via the Google Cloud Accelerated Platforms GitHub repository.
– Contains Terraform code, documentation, and example use cases to facilitate quick deployment of popular workloads.
This architecture significantly lowers the barrier for companies attempting to integrate AI into their production systems by providing an easy-to-use and secure framework. This represents not only a shift towards automated, high-performing inference deployments but also a strong commitment to maintaining robust security and efficient operations in AI and cloud environments.
By adopting this reference architecture, AI professionals can build secure, scalable infrastructures that expedite their AI adoption while adhering to best practices in cloud computing and infrastructure security.