Getting Started with the CNCF Cloud-Native AI Stack: A Practical Guide for New Engineers

If you’re a new engineer looking to understand the CNCF Cloud-Native AI (CNAI) ecosystem, you’re entering at exactly the right moment. The stack has matured from experimental projects into production-ready infrastructure that powers real AI workloads at scale.

But here’s the challenge: the ecosystem is vast, the documentation is scattered, and it’s not immediately obvious where to start or how the pieces fit together. This guide gives you a practical learning path based on how these technologies actually compose in production systems.

Understanding the Architecture: Three Layers

The CNCF CNAI stack is best understood as three distinct layers, each solving a specific problem:

Layer 1: Model Serving (KServe)
The interface between your models and the outside world. This is where inference requests arrive and responses are returned.

Layer 2: Model Execution (vLLM, llm-d)
The high-performance engines that actually run your models, managing GPU memory, batching requests, and optimizing throughput.

Layer 3: Multi-Cluster Orchestration (Karmada)
The coordination layer that distributes workloads across multiple Kubernetes clusters, handling placement, failover, and policy enforcement.

Each layer builds on the one below it. You can’t effectively learn Karmada without understanding KServe, and you can’t understand KServe without knowing what vLLM does. The learning path follows the dependency chain.

Start with KServe: The Foundation

KServe is where your journey should begin. It’s the CNCF standard for model serving on Kubernetes, and it teaches you the fundamental operational flow: model artifact → inference service.

What KServe Does

KServe abstracts away the complexity of deploying models as scalable services. Instead of manually configuring deployments, services, ingress rules, and autoscaling policies, you declare what you want:

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: my-model
spec:
  predictor:
    model:
      modelFormat:
        name: pytorch
      storageUri: s3://my-bucket/my-model

KServe handles:

• Autoscaling: Scale to zero when idle, scale up under load
• Request routing: Canary deployments, A/B testing, traffic splitting
• Multi-model serving: Run multiple models on shared infrastructure
• Protocol standardization: Consistent inference API across model types

Your First KServe Deployment

The fastest way to learn KServe is to deploy a simple model end-to-end. The KServe quickstart walks you through deploying a scikit-learn model in minutes.

What you’ll learn:

• How Kubernetes primitives (Deployments, Services, HPA) combine to form a serving platform
• How models are loaded from storage (S3, GCS, local volumes)
• How autoscaling responds to traffic patterns
• How to monitor inference latency and throughput

This hands-on experience builds intuition for how AI workloads behave in production. You’ll see firsthand why autoscaling matters, what happens when a model takes too long to load, and how request queuing affects latency.

Going Deeper with KServe

Once you’ve deployed your first model, explore:

• Canary deployments: Route 10% of traffic to a new model version
• Transformers: Pre-process requests and post-process responses
• Explainability: Integrate model explanation tools
• Multi-model serving: Run multiple models on the same pod

The KServe documentation is comprehensive, and the v0.15 release announcement highlights recent improvements for LLM serving.

Learn vLLM and llm-d: The Execution Layer

Once you understand how KServe exposes models as services, it’s time to dive into the execution layer. This is where the actual inference happens, and performance matters.

vLLM: High-Performance LLM Inference

vLLM is the inference engine that powers many production LLM systems. It’s not just “another inference server”—it’s a fundamentally different approach to serving large language models efficiently.

Key innovations:

PagedAttention: Traditional LLM serving wastes massive amounts of GPU memory due to fragmentation. vLLM treats the key-value cache like virtual memory pages, eliminating fragmentation and packing more concurrent requests onto the same GPU. This isn’t a minor optimization—it’s a 23x throughput improvement over naive PyTorch implementations.

Continuous batching: Instead of waiting for all sequences in a batch to complete before starting new ones, vLLM uses rolling batches. As soon as one sequence finishes, a new one takes its place. The GPU never sits idle waiting for stragglers.

Tensor parallelism: For models too large to fit on a single GPU, vLLM automatically shards them across multiple GPUs with minimal overhead.

GPU-aware scheduling: vLLM understands GPU topology and schedules work to minimize data movement between GPUs.

Your First vLLM Deployment

Start by deploying a small LLM (Llama 3.2 1B or similar) with vLLM under KServe. The vLLM quickstart guide provides a practical walkthrough.

What you’ll learn:

• How to configure vLLM for different model sizes
• How GPU memory limits affect batch size and throughput
• How to benchmark latency and throughput
• How continuous batching improves utilization

Then experiment with different configurations:

• Single GPU: Baseline performance
• Multi-GPU: Tensor parallelism for larger models
• CPU-only: Understand the performance gap (it’s massive)

This hands-on work is where you develop practical intuition for how containerization, resource requests, and GPU topology impact model performance. The vLLM architecture deep-dive explains the internals if you want to understand how it works under the hood.

llm-d: Distributed LLM Inference

llm-d is a Kubernetes-native distributed inference framework designed for serving large language models at scale. While vLLM focuses on single-node optimization, llm-d extends inference across multiple nodes and clusters.

Key capabilities:

Distributed inference: Split large models across multiple nodes when they don’t fit on a single machine

Kubernetes-native: Built specifically for Kubernetes with native resource management and scheduling

Production-grade: Designed by contributors to Kubernetes and vLLM with production deployment patterns built in

Hardware flexibility: Works across different GPU types and accelerators

The llm-d introduction and getting started guide provide practical walkthroughs for deploying distributed inference workloads.

Model Packaging with OCI Artifacts

For model packaging and distribution, the ecosystem is converging on OCI artifacts—the same format used for containers. ModelPack, accepted into CNCF Sandbox in June 2025, provides a vendor-neutral standard for packaging ML models as OCI objects.

This approach brings several benefits:

• Familiar tooling: Use the same registries and workflows as containers
• Versioning: Semantic versioning for model releases
• Multi-platform support: Same artifact runs on different hardware
• Security: Leverage existing container security scanning and signing

Docker’s Model Runner specification demonstrates how OCI artifacts simplify model distribution and deployment.

Understand Karmada: Multi-Cluster Federation

Once you’re comfortable with model serving and execution, the next layer is multi-cluster orchestration. This is where Karmada comes in.

Why Multi-Cluster Matters

Real production environments don’t have one Kubernetes cluster. They have:

• Multiple data centers for redundancy
• Edge locations for low-latency inference
• Regional deployments for data sovereignty
• Air-gapped environments for security

Karmada provides the control plane that manages workloads across this fleet of clusters.

What Karmada Does

Karmada extends Kubernetes concepts to multiple clusters:

Cross-cluster scheduling: Place workloads based on capacity, cost, latency, and policy constraints

Propagation policies: Define which clusters should run which workloads

Failover: Automatically migrate workloads when clusters fail

Federated resources: Manage quotas, HPA, and services across clusters

Identity and RBAC: Consistent access control across the fleet

Your First Karmada Deployment

The Karmada quickstart walks you through deploying a federated application across multiple clusters.

Start simple:

1. Deploy a KServe inference service to a single cluster via Karmada
2. Add a second cluster and configure propagation policies
3. Observe how Karmada distributes replicas across clusters
4. Simulate a cluster failure and watch failover

What you’ll learn:

• How federated scheduling differs from single-cluster scheduling
• How to express placement constraints (data locality, compliance boundaries)
• How traffic routing works across clusters
• How to monitor multi-cluster workloads

The Karmada comprehensive tutorial provides deeper coverage of advanced features.

Build an End-to-End Project

The most effective way to cement your understanding is to build a complete system that exercises all three layers. Here’s a practical project:

Goal: Deploy a federated LLM inference service that serves requests from multiple regions with automatic failover.

Steps:

1. Package a model as an OCI artifact
   • Choose a small LLM (Llama 3.2 1B)
   • Package it using ModelPack or Docker Model Runner
   • Push to a container registry
2. Deploy with vLLM under KServe
   • Create an InferenceService that uses vLLM runtime
   • Configure autoscaling (min 1, max 5 replicas)
   • Expose an inference endpoint
   • Benchmark throughput and latency
3. Replicate across clusters with Karmada
   • Set up two Kubernetes clusters (use kind for local testing)
   • Install Karmada
   • Create a propagation policy that deploys to both clusters
   • Configure multi-cluster service discovery
4. Test failure scenarios
   • Send inference requests through the multi-cluster service
   • Simulate cluster failure (shut down one cluster)
   • Verify requests continue to be served
   • Measure failover time

This project gives you a working mental model of how the CNAI components interlock. You’ll understand:

• How models flow from artifact to running service
• How execution engines optimize GPU utilization
• How multi-cluster orchestration provides resilience
• Where the complexity lives and what can go wrong

The Broader Ecosystem

Once you’ve mastered the core stack (KServe, vLLM, LLMD, Karmada), you can explore related CNCF technologies that extend the platform:

Ray: Distributed compute framework for training and batch inference

Volcano: Advanced GPU scheduling and batch job management

Kubeflow: End-to-end ML pipelines from training to serving

Argo Workflows: Workflow orchestration for ML pipelines

Milvus: Vector database for RAG applications

Prometheus + Grafana: Observability for AI workloads

Each of these projects solves a specific problem in the AI infrastructure stack. The CNCF landscape shows how they all fit together.

Learning Resources

Here are the resources I recommend for going deeper:

Official Documentation:

• KServe Documentation
• vLLM Documentation
• Karmada Documentation
• CNCF Cloud Native AI White Paper

Tutorials and Guides:

• The Ultimate Guide to Production Model Serving on Kubernetes
• Building Efficient LLM Inference with the Cloud Native Quartet
• vLLM Quickstart: High-Performance LLM Serving
• Inside vLLM: Anatomy of a High-Throughput LLM Inference System
• Karmada: Comprehensive Tutorial

Community:

• CNCF Slack - Join #kserve, #karmada channels
• KServe GitHub Discussions
• Karmada GitHub Discussions

Ramp-Up Strategy Summary

If I had to distill this into a practical learning plan:

Week 1-2: KServe Fundamentals

• Deploy your first inference service
• Experiment with autoscaling and traffic splitting
• Monitor latency and throughput
• Understand the Kubernetes primitives involved

Week 3-4: vLLM and Model Execution

• Deploy an LLM with vLLM under KServe
• Benchmark different configurations (CPU, single GPU, multi-GPU)
• Understand PagedAttention and continuous batching
• Learn to tune for latency vs throughput

Week 5-6: llm-d and Distributed Inference

• Set up llm-d for multi-node inference
• Deploy models that span multiple GPUs/nodes
• Understand distributed inference tradeoffs
• Experiment with OCI artifact packaging for models

Week 7-8: Karmada and Multi-Cluster

• Set up a multi-cluster environment
• Deploy federated inference services
• Configure propagation policies
• Test failover scenarios

Week 9-10: End-to-End Project

• Build a complete federated LLM service
• Implement monitoring and observability
• Document your architecture
• Share your learnings

Key Principles

As you learn the CNCF CNAI stack, keep these principles in mind:

Learn one layer at a time: Don’t try to understand everything at once. Master serving before execution, execution before federation.

Use hands-on deployments: Reading documentation is necessary but insufficient. Deploy real workloads and observe their behavior.

Focus on performance characteristics: Don’t just learn the APIs—understand why PagedAttention matters, why continuous batching improves throughput, why multi-cluster adds complexity.

Build incrementally: Start with simple deployments and add complexity gradually. Single model → multiple models → multi-cluster → production-grade observability.

Understand the tradeoffs: Every architectural decision involves tradeoffs. Why KServe over raw Kubernetes? Why vLLM over TensorRT-LLM? Why Karmada over manual cluster management?

The Bottom Line

The CNCF CNAI ecosystem provides production-ready infrastructure for running AI workloads at scale. The stack is mature, the patterns are established, and the community is active.

But it’s not simple. These are distributed systems with real complexity. The learning curve is steep, especially if you’re new to Kubernetes, GPU programming, or distributed systems.

The good news: you don’t need to learn everything at once. Start with KServe. Deploy a model. See it scale. Then move to vLLM and understand execution. Then tackle Karmada and multi-cluster orchestration.

Each layer builds on the previous one. Each hands-on deployment deepens your understanding. Each failure teaches you something about how production systems behave.

The CNCF CNAI stack is how modern AI infrastructure is built. Learning it gives you the foundation to operate, optimize, and extend cloud-native AI platforms at scale.

Start with KServe. Deploy a model today. The rest will follow.

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What’s your experience with the CNCF CNAI stack? What challenges have you encountered? I’d love to hear your perspective. Reach out on LinkedIn or drop a comment below.