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Deploying Open-Source LLMs on Kubernetes: A Comprehensive Guide
TechnicalBy Robert Cronin26 min read

Deploying Open-Source LLMs on Kubernetes: A Comprehensive Guide

Open SourceLLMsKubernetesComprehensiveGuide

Deploying open-source large language models (LLMs) on Kubernetes involves careful planning around architecture, scaling, security, and tooling. This guide outlines best practices, security measures, serving frameworks, scaling techniques, workflow automation, cost optimizations, and cloud-specific tips for running open-source LLMs in Kubernetes.

Best Practices for LLM Deployment on Kubernetes

  • Containerization & Resource Management: Package your LLM and its dependencies into a Docker image and define resource requests/limits for CPU, memory, and GPU in the pod spec. This ensures each LLM pod has the required resources without starving others (Deploying LLMs on Kubernetes | samzong) (Deploying LLMs on Kubernetes | samzong). Use Kubernetes Resource Quotas to cap total resource usage per namespace and avoid cluster exhaustion (Deploying LLMs on Kubernetes | samzong).

  • Optimal Deployment Architecture: Consider whether to scale up or out based on model size. Scale-up means using multiple containers (e.g. sharded model parts) in one pod to utilize multiple GPUs on a single node (model parallelism), whereas scale-out runs multiple replica pods (data parallelism) across nodes (Deploying LLMs on Kubernetes | samzong). For very large models that exceed a single GPU’s memory, use model sharding or pipeline parallelism to split the model across GPUs or nodes (Deploying LLMs on Kubernetes | samzong). This allows serving LLMs with billions of parameters by coordinating multiple pods or devices.

  • Model Serving Strategies: Use specialized inference servers or libraries optimized for LLMs. For example, NVIDIA’s Triton Inference Server or Hugging Face’s Text Generation Inference (TGI) can greatly improve throughput and latency for LLM serving. Some high-performance inference engines (like vLLM) implement token batching and caching to serve many requests efficiently (Deploying LLMs on Kubernetes | samzong). These tools can be integrated into Kubernetes deployments as the model server backend within your pods.

  • Autoscaling: Leverage Kubernetes autoscaling to handle variable load. Enable Horizontal Pod Autoscaler (HPA) to add or remove LLM pod replicas based on CPU/GPU utilization or custom metrics like request latency (Deploying LLMs on Kubernetes | samzong). For example, you might autoscale on GPU utilization or queue length to ensure low-latency responses under load. Combine HPA with Cluster Autoscaler so the cluster can provision new GPU nodes when needed (Deploying LLMs on Kubernetes | samzong). Some model serving frameworks (e.g. KServe via Knative) even support scale-to-zero, spinning down pods during idle periods to save costs (Introduction | Kubeflow).

  • Monitoring & Logging: Establish robust monitoring for your LLM services. Use Prometheus to collect metrics on CPU, memory, GPU usage, response latency, and throughput (Deploying LLMs on Kubernetes | samzong). Grafana can then display real-time dashboards for these metrics and trigger alerts when thresholds are breached (e.g. high GPU or memory usage) (AI/ML in Kubernetes Best Practices: The Essentials | Wiz) (AI/ML in Kubernetes Best Practices: The Essentials | Wiz). Implement centralized logging (e.g. EFK stack – Elasticsearch/Fluentd/Kibana) so that you can trace user queries and model outputs for debugging. Comprehensive observability makes it easier to troubleshoot performance issues or errors in your LLM inference service.

Security Concerns and Protections

Deploying LLMs in production requires addressing security at multiple layers:

  • Model Integrity: Protect the integrity of your model artifacts and containers. Use image scanning tools to detect vulnerabilities in the LLM image before deployment (AI/ML in Kubernetes Best Practices: The Essentials | Wiz). Implement image signing (e.g. with Cosign) to ensure only trusted images are used—digital signatures and checksums can verify that model images or weight files haven’t been tampered with (AI/ML in Kubernetes Best Practices: The Essentials | Wiz). Store model files in secure storage (such as a private S3 bucket or encrypted PersistentVolume) and enable encryption at rest. Monitor for data drift or poisoning attacks by validating inputs in production; drift detection libraries (like Alibi Detect) can trigger alerts or retraining when incoming data distribution shifts (AI/ML in Kubernetes Best Practices: The Essentials | Wiz) (AI/ML in Kubernetes Best Practices: The Essentials | Wiz).

  • Secure Access Control: Restrict who and what can interact with the LLM deployment. Apply Kubernetes Role-Based Access Control (RBAC) so that only authorized users or service accounts can deploy or modify the LLM pods (AI/ML in Kubernetes Best Practices: The Essentials | Wiz). Within your application, secure the LLM’s API endpoint behind an authentication layer (e.g. API Gateway or service mesh with mTLS) to prevent unrestricted public access. Use Kubernetes Secrets to hold sensitive info like API keys or database credentials needed by the model, instead of hardcoding them in images or configs (Deploying LLMs on Kubernetes | samzong).

  • Network Policies: Implement a zero-trust network model in the cluster. Define Kubernetes NetworkPolicies to restrict traffic to and from the LLM pods (AI/ML in Kubernetes Best Practices: The Essentials | Wiz). For example, you can allow only the ingress from your application namespace or ingress controller to the LLM service, and block all other access (AI/ML in Kubernetes Best Practices: The Essentials | Wiz) (AI/ML in Kubernetes Best Practices: The Essentials | Wiz). Isolate LLM workloads in a separate namespace or even a dedicated cluster segment so that if another app is compromised, it can’t freely communicate with the LLM pod. This limits lateral movement and protects the LLM from unauthorized requests.

  • Pod Security and Runtime: Enable Pod Security Standards or PodSecurityPolicies to enforce least-privilege at the pod level (e.g. preventing privileged mode or host network access) (AI/ML in Kubernetes Best Practices: The Essentials | Wiz). Ensure the LLM container runs as a non-root user. Use read-only file systems for containers if possible (except where model writes are needed) to reduce risk of malicious changes. For an extra layer, consider running the LLM behind a service mesh which can encrypt internal traffic (mTLS) and provide fine-grained policies.

  • Audit and Monitoring: Continuously monitor security events. Kubernetes audit logs and tools like Wiz can track configuration drifts, detect any privileged escalations, or even spot secrets accidentally exposed in the environment (AI/ML in Kubernetes Best Practices: The Essentials | Wiz). Set up alerts for suspicious spikes in requests (which could indicate abuse, e.g. trying to prompt the model with malicious input) and use rate limiting on your ingress to mitigate DDoS attempts.

Frameworks and Tools for Model Serving

Several open-source frameworks can simplify serving LLMs on Kubernetes. Below is a comparison of popular tools:

  • KServe (KFServing): A Kubernetes-native model serving platform originally from Kubeflow. KServe uses Custom Resources to deploy models and handles autoscaling, networking, health checks, and even canary rollouts for you (Introduction | Kubeflow). It supports serverless inference with Knative, allowing scale-to-zero and rapid scaling based on traffic (Introduction | Kubeflow). KServe is framework-agnostic with built-in support for TensorFlow, PyTorch, XGBoost, scikit-learn, LightGBM, ONNX, etc., and you can bring your own inference container for any model (Empower conversational AI at scale with KServe | Red Hat Developer) (Empower conversational AI at scale with KServe | Red Hat Developer). It abstracts away much of the complexity of serving at scale, offering a high-level interface for prediction, pre/post-processing, and even explanation out-of-the-box (Introduction | Kubeflow). Use case: If you want a plug-and-play serving solution on Kubernetes with autoscaling and multi-model support, KServe is a strong choice (Top 8 Machine Learning Model Deployment Tools in 2024).

  • Seldon Core: An open-source MLOps framework focusing on advanced deployment scenarios. Seldon Core turns Kubernetes into a microservice graph of model components. It allows you to deploy inference pipelines with multiple steps (chaining models, transformers, routers) via a custom CRD (SeldonDeployment) and a flexible service orchestrator. Seldon supports many frameworks (SKLearn, XGBoost, TF, PyTorch, etc.) and can integrate with MLflow or NVIDIA Triton servers for optimized serving (KServe vs. Seldon Core | Superwise ML Observability) (KServe vs. Seldon Core | Superwise ML Observability). It excels at production rollout features: you can do A/B testing of model versions, shadow deployments, canary rollouts, and even add explainers and outlier detectors as part of the inference graph (Top 8 Machine Learning Model Deployment Tools in 2024) (Top 8 Machine Learning Model Deployment Tools in 2024). Seldon requires Kubernetes expertise to set up, but it’s very powerful for complex scenarios. Use case: When you need custom inference logic (multiple models or pre/post-processing steps) and advanced monitoring/explainability in your serving platform.

  • Kubeflow: Kubeflow is an end-to-end machine learning platform on Kubernetes, which includes components for model training, pipeline orchestration, and serving. For serving, Kubeflow formerly used KFServing (now KServe) as a native component (Top 8 Machine Learning Model Deployment Tools in 2024). Essentially, Kubeflow’s Model Serving adds a user-friendly interface and integration to deploy models via KServe in a full MLOps pipeline. Kubeflow is ideal if you want a full-stack solution – from notebooks to training to deploying LLMs – with a central dashboard. Use case: Teams looking for a comprehensive ML platform can use Kubeflow to manage experiments, pipelines, and serve models (including LLMs) in one cohesive environment (Top 8 Machine Learning Model Deployment Tools in 2024).

  • Ray Serve: Part of the Ray distributed computing framework, Ray Serve is a scalable model serving library that can run on Kubernetes (via Ray clusters). It is very flexible – you can serve not just ML models but arbitrary Python business logic and compose multiple models or pipeline steps in code (Top 8 Machine Learning Model Deployment Tools in 2024) (Top 8 Machine Learning Model Deployment Tools in 2024). Ray Serve supports scaling across nodes and offers fractional GPU sharing and multiplexing, meaning you can deploy multiple lightweight models on one GPU or split resources more finely than one model-per-GPU. It integrates with FastAPI to easily create web endpoints for inference (Top 8 Machine Learning Model Deployment Tools in 2024). However, using Ray Serve means running a Ray cluster (adding some complexity) and it may lack some out-of-the-box features like built-in canary deployments or logging that KServe/Seldon have (Top 8 Machine Learning Model Deployment Tools in 2024). Use case: Useful when you already use Ray for other distributed tasks, or need to serve many models and handle dynamic request routing in code (e.g., ensemble of LLMs).

  • Other Tools: There are additional open-source serving solutions:

    • TorchServe (for PyTorch models) – simplifies serving PyTorch LLMs with multi-model support and REST/gRPC endpoints. It supports model versioning, batch inference, and can integrate with Kubernetes (there are helm charts available) (Top 8 Machine Learning Model Deployment Tools in 2024) (Top 8 Machine Learning Model Deployment Tools in 2024).
    • Triton Inference Server – NVIDIA’s optimized server that supports multi-framework models (TensorRT, PyTorch, TensorFlow, ONNX, etc.) and is optimized for GPUs. Triton is often used to serve large models (including LLMs) efficiently and can be deployed on K8s with official helm charts. It can handle dynamic batching, which is useful for high-throughput LLM workloads.
    • OpenLLM – an open-source platform (from BentoML) specifically for operating LLMs in production. It provides tools to run inference, fine-tune, and deploy LLMs easily, and can containerize LLMs like Dolly, Llama 2, etc. for Kubernetes (Deploying LLMs on Kubernetes | samzong).
    • BentoML – a model serving framework that lets you package models with a Python service definition. BentoML can containerize the model server and handle runtime, and you can deploy that container to Kubernetes. It’s not K8s-native (no CRD), but can be combined with Kubernetes for scaling.

Choose a tool based on your team’s needs: if you want Kubernetes-native declarative deployment, KServe or Seldon are fitting; if you favor Python-driven deployment and distributed computing, consider Ray Serve or BentoML. Often, LLM-specific optimizations (like those in Triton, FasterTransformer, TGI, or vLLM) can be used in combination with these serving platforms to achieve both manageability and performance.

(Introduction | Kubeflow) KServe’s architecture layers Kubernetes and Knative under a model inference layer. It handles prediction, pre/post-processing, and monitoring for various ML frameworks, while integrating with GPU/CPU infrastructure (Introduction | Kubeflow).

Scaling Strategies for LLM Inference

Serving large language models efficiently requires smart scaling strategies to handle both model size and traffic load:

  • Horizontal Scaling and Load Balancing: For handling high request volumes, run multiple replicas of your LLM service behind a Kubernetes Service. The service will load-balance requests across pods. Combine this with HPA so that when traffic increases, new pods spawn automatically. Ensure your LLM container can handle a certain number of concurrent requests (through threading or async IO) to maximize GPU utilization. If the model runtime is single-threaded, you may scale out with more pods instead of increasing pod concurrency. Kubernetes Services use round-robin load balancing by default, which works for stateless inference requests. In some LLM cases (like streaming token APIs), you may need session-affinity or more intelligent load balancers; but generally, a ClusterIP/LoadBalancer service suffices for distributing requests.

  • Multi-GPU and Multi-Node Parallelism: For extremely large open-source LLMs (billions of parameters) that cannot fit on one GPU, leverage parallelism techniques. Libraries like NVIDIA TensorRT-LLM, DeepSpeed-Inference, or Hugging Face Accelerate can partition the model across multiple GPUs. You can deploy a single pod with multiple GPU containers (or one container using multiple GPUs) to load different shards of the model (model parallelism). Kubernetes supports pod spec affinity to schedule such pods on nodes with multiple GPUs or across specific nodes. For even larger models that require multiple machines, you can use a coordinated deployment: e.g., NVIDIA Triton with multi-node configuration or the Ray framework to coordinate inference across pods. In a recent AWS example, Triton together with TensorRT-LLM enabled serving a 405B-parameter Llama model by sharding it across GPUs on multiple EC2 instances in an EKS cluster (Scaling your LLM inference workloads: multi-node deployment with TensorRT-LLM and Triton on Amazon EKS | AWS HPC Blog). This kind of sharded deployment requires a backend that supports distributed inference (Triton, HuggingFace’s model.parallelize(), or PyTorch RPC). Always ensure the inter-pod network latency is low (consider placing pods on the same availability zone or using high-bandwidth cluster networking) because cross-node communication overhead can impact inference speed.

  • GPU Optimization: Maximize GPU utilization since GPUs are often the costliest resource for LLMs. Use techniques like quantization (reducing model precision from 16-bit to 8-bit or 4-bit) to shrink model size and increase inference speed, often with negligible impact on quality. Quantized open-source LLMs can sometimes run on smaller GPUs or even on CPUs with acceleration. Also consider using NVIDIA’s TensorRT to optimize the model graph and cache kernels for faster inference on GPU. Ensure to enable CUDA’s multi-stream or use frameworks that support concurrent execution to keep the GPU busy. If using newer GPUs (A100/A30), take advantage of Multi-Instance GPU (MIG) to partition a single physical GPU into multiple isolated instances. MIG allows running concurrent LLM inferencers on fractions of a GPU, improving overall utilization for smaller models (Getting Kubernetes Ready for the NVIDIA A100 GPU with Multi-Instance GPU | NVIDIA Technical Blog). For example, an A100 can be split into 7 MIG slices – you could schedule 7 lightweight model pods on one GPU, each thinking it has its own GPU slice (useful if serving many smaller LLM variants or handling many low-QPS models on one host).

  • Autoscaling Policies: Tuning the autoscaler for LLM workloads is important. GPU and memory usage are key metrics – CPU-based autoscaling might not capture load if the model is GPU-bound. You can use custom metrics (with Prometheus Adapter or KEDA) such as GPU utilization or request queue length to scale pods. Also define buffer capacity – for instance, maintain at least one spare replica to handle sudden spikes (so-called “cold” replica ready to take traffic). Conversely, set a cool-down period so pods don’t scale down too quickly during transient dips (to avoid thrashing when load spikes again). If using KServe, its Knative integration can scale down to zero on idle, but ensure your first request cold-start time is acceptable or use a warm-up trigger.

  • Inference Batch Processing: If real-time latency isn’t strict, you can batch multiple requests together to improve throughput. Some frameworks (Triton, TorchServe, and vLLM) support micro-batching: accumulating, say, 32 small requests and processing them in one forward pass yields higher GPU utilization. This reduces per-request overhead and can drastically increase throughput/$$. In Kubernetes, you might implement this with a small queue within your inference service or use event-driven systems (like pulling requests from a Kafka or Redis queue). Batch processing trades off latency, so it’s optional for real-time but very effective for asynchronous use cases.

Deployment Workflows and Automation

Maintaining and updating LLM deployments require robust workflows:

  • CI/CD for Models: Set up continuous integration/continuous deployment pipelines to automate model releases. For example, when a data scientist pushes a new version of the model (or its Docker image) to Git or your container registry, have a CI pipeline (Jenkins, GitHub Actions, GitLab CI, etc.) that runs tests (e.g., basic sanity checks or performance benchmarks on the new model) and then updates the Kubernetes manifests. Embrace infrastructure-as-code: your model’s deployment YAML or Helm chart should be version-controlled. This allows code reviews for changes to resource requests or scaling parameters, and traceability of which model version is running. Canary deployments are recommended for LLM updates: deploy the new model version alongside the old and route a small percentage of traffic to it, to compare performance and correctness before full rollout (both KServe and Seldon natively support canary routing policies (Introduction | Kubeflow)).

  • Helm Charts and Templates: Package your Kubernetes manifests (Deployment, Service, HPA, etc.) into a Helm chart or Kustomize template. This makes it easy to deploy the whole LLM stack repeatedly (e.g., in different environments or for different model variants) with parameterized values (like model name or resource sizes). Helm charts for common tools (KServe, Seldon, TorchServe) are often provided by the community, which can jump-start your setup. Using charts also simplifies upgrades and rollbacks of your LLM services.

  • GitOps: Consider a GitOps approach for managing deployments. Tools like Argo CD or Flux continuously reconcile the cluster state with configs in a Git repo. You would commit the updated model Deployment or InferenceService manifest to Git, and the GitOps operator applies it to Kubernetes. This brings benefits like auditability (Git history is the change log) and easy rollback (revert the git commit). GitOps shines in multi-environment scenarios and ensures no one is manually tweaking the cluster – everything goes through code. In the context of LLMs, you might store not just Kubernetes manifests but also a reference to the model artifact or image tag in Git, so the exact model version is part of the desired state.

  • Containerization Best Practices: Building the Docker image for an LLM requires special care due to image size. Exclude development dependencies and use a slim base image (e.g., python:3.10-slim). If the model weights are huge (several GBs), you might not bake them into the image – instead mount them via a volume or have an init container download them from cloud storage at startup. This keeps image sizes manageable and allows updating the model file without rebuilding the image. Always pin versions of libraries (to avoid unexpected changes) and verify that your image works on the target GPU (CUDA and driver versions compatible). Use stable tags or digests for images in production to avoid “latest” pulling a surprise update (AI/ML in Kubernetes Best Practices: The Essentials | Wiz) (AI/ML in Kubernetes Best Practices: The Essentials | Wiz). Incorporate vulnerability scanning in your CI (using tools like Trivy or Clair) to catch issues in the image before deployment (AI/ML in Kubernetes Best Practices: The Essentials | Wiz).

  • Continuous Monitoring & A/B Testing: Once deployed, continuously monitor the model’s performance (both system metrics and prediction quality). If you have multiple models (say an older and a new version), you can route a portion of traffic to each (A/B testing) and compare outputs or user feedback. Seldon Core has built-in support for experiments like this, or you can implement it at the application level. Use the feedback to quickly iterate – your CI/CD pipeline should allow fast redeployment of improved models. Also, implement an automated rollback in your CD pipeline: if the new model deployment is not healthy (probe failures) or performs worse on key metrics, the pipeline or orchestrator can revert to the last good version.

Cost Optimization Techniques

Running LLMs, especially on GPUs, can be expensive. Here are strategies to manage costs:

  • Spot/Preemptible Instances: Leverage spot instances (AWS) or preemptible VMs (GCP) for non-critical workloads. These come at a huge discount (up to ~90% cheaper than on-demand prices) (Maximizing GKE discounts: Kubernetes cost optimization strategies). For example, you might run a fleet of spot GPU nodes for your inference autoscaler to use, accepting that they can be reclaimed occasionally. Ensure your HPA and cluster autoscaler are configured to handle sudden node loss (the pods will be rescheduled on remaining nodes). Not all workloads can tolerate interruption, but if your LLM inference is stateless and you have spare capacity to retry requests, spot instances can dramatically cut costs. Another approach is to use spot instances for redundant capacity (overflow traffic or batch jobs) while keeping a baseline on reserved instances.

  • Resource Right-Sizing: Continuously measure how much CPU, memory, and GPU your LLM actually uses, and adjust requests/limits accordingly. Over-provisioning resources (e.g., allocating 2 GPUs when the model only uses 1) wastes money. If your model can run with 16GB GPU memory, don’t use a 40GB GPU – choose a smaller GPU instance type or limit the GPU memory via virtualization if possible. Similarly, if the container never uses more than 4 cores, lower the CPU requests so the scheduler can pack pods more tightly (while still leaving headroom). The Vertical Pod Autoscaler (VPA) in recommendation mode can help suggest better resource sizes over time (Deploying LLMs on Kubernetes | samzong).

  • Scaling to Zero & On-Demand: For infrequently used models (e.g., an LLM that is accessed a few times a day), configure the deployment to scale down to 0 replicas when idle, so you’re not paying for idle GPU time. KServe’s Knative-based serving supports scale-to-zero natively (Introduction | Kubeflow). When a request comes in, it will cold-start a pod (incurring a startup delay but saving cost during idle hours). If cold starts are a concern, an alternative is to schedule certain LLM services only on-demand: use a job queue and spin up a job or pod when a request arrives (this is more for batch processing scenarios).

  • GPU Sharing: As noted, NVIDIA MIG can partition a GPU, allowing multiple pods to share one physical GPU with hardware isolation (Getting Kubernetes Ready for the NVIDIA A100 GPU with Multi-Instance GPU | NVIDIA Technical Blog). If you have an A100 40GB card and your model only needs ~10GB, you can run 4 MIG instances and put 4 model pods on that card, effectively quadrupling throughput per dollar. Even without MIG, you can run multiple containers on one GPU by enabling Nvidia’s time-slicing (the default behavior if you simply schedule two pods to the same GPU, though they will contend). However, time-slicing can lead to unpredictable latency; MIG is preferable for guaranteed slices. Some K8s device plugin configurations allow advertising fractional GPUs as schedulable resources (e.g., nvidia.com/gpu-memory), but those are advanced setups. The key idea is to drive GPU utilization close to 90-100% by not leaving GPUs underused – consolidate workloads if they don’t fill the GPU.

  • Optimize Models: Use optimized model versions to reduce infrastructure needs. Open-source LLMs often have community variants that are compressed or distilled. For example, instead of running a 13B parameter model at full size, a distilled 6B model fine-tuned on similar data might achieve needed accuracy at half the compute cost. Likewise, use tools like Hugging Face Optimum or ONNX Runtime to get better inference performance on CPUs or lower-end GPUs, letting you use cheaper hardware. Running LLMs on CPU is much slower but if real-time speed isn’t needed, high-core count CPU nodes (which are cheaper than GPUs) could be an option – especially with 4-bit quantization, some smaller LLMs can run on CPU reasonably.

  • Scheduling Strategies: Align your resource usage with cost-saving opportunities. For instance, if your cloud provider has varying prices or capacity, schedule non-urgent LLM jobs during off-peak hours. On-prem, you might batch jobs at night to utilize power when rates are lower. Use the cluster autoscaler to scale nodes down at night if traffic drops (to not pay for unused nodes). Also consider reserved instances or savings plans (cloud commit discounts) for steady-state portions of your workload – e.g., keep a base level of 2 GPU nodes on a 1-year reservation, and autoscale additional nodes on demand or via spot for peaks. This hybrid approach ensures a low baseline cost with capacity to burst as needed.

Cloud-Specific Considerations

Deploying LLMs on Kubernetes will have some cloud-specific nuances depending on the environment:

  • AWS (EKS): Amazon EKS is a managed Kubernetes control plane – you are responsible for worker nodes. Use EKS optimized AMIs with GPU support for your node groups (these come with NVIDIA drivers pre-installed for Tesla GPUs). Alternatively, install the NVIDIA device plugin as a DaemonSet to manage GPU scheduling. Leverage AWS IAM Roles for Service Accounts (IRSA) if your model pods need to access AWS resources like S3 to download weights – this way, the pod can assume an IAM role rather than hardcoding AWS keys. For storage of large models, AWS EFS (a shared file system) or FSx for Lustre can be mounted as persistent volumes accessible by all replicas (so you don’t duplicate 50GB model files per pod). When scaling across multiple AZs, be mindful that inter-AZ latency can affect performance; you might deploy LLM pods in a single AZ and use an internal ELB (Service type LoadBalancer) for traffic. AWS also offers Inferentia chips and Trn1 instances for inference – though those are for specific model types (not widely used for custom LLMs yet), the open-source Neuron SDK could be explored for cost-effective hardware acceleration. In terms of autoscaling, use the Cluster Autoscaler on EKS (or Karpenter) to automatically add EC2 GPU instances when HPA demands more pods. Ensure your instance type has the right GPU (e.g., g5 instances for NVIDIA A10G, p4d for A100s, etc.). AWS spot instances are a great fit here – for example, run managed node groups with spot across multiple instance types to increase odds of getting capacity. Finally, integrate with CloudWatch for logging and monitoring – you can forward Kubernetes logs to CloudWatch Logs and metrics to CloudWatch or Prometheus.

  • GCP (GKE): Google Kubernetes Engine offers a seamless experience for GPU workloads. You can create node pools with NVIDIA GPUs (Tesla T4, V100, A100, etc.), and GKE can optionally install the NVIDIA drivers automatically. GCP’s strengths for LLM serving include TPUs if your model is compatible (though TPU support in Kubernetes may require the use of the Cloud TPU Kubernetes add-on, and open-source LLM frameworks like JAX/FLAX or TensorFlow to utilize them). GKE has Preemptible VMs which are like AWS spot – use them for cost savings on GPU nodes (they last up to 24 hours). Store models in Cloud Storage buckets and access them via the GCS Fuse CSI driver or download in init containers. GKE Autopilot can manage infrastructure for you, but currently Autopilot has limited GPU support and might not be ideal for large LLM deployments (standard GKE offers more control). Make use of Google Cloud’s monitoring – GKE can send metrics to Cloud Monitoring; you might create custom dashboards for GPU usage. Also, Google has published specific guidance on optimizing LLM inference on GKE with open-source tools like TGI and vLLM (Best practices for optimizing large language model inference with ...), which can be a valuable reference for squeezing the most out of GPU nodes. If you use GKE’s ingress, you can integrate Cloud Armor for an extra security layer (protect against common web attacks hitting your model endpoint).

  • Azure (AKS): Azure Kubernetes Service similarly supports GPU node pools (NC series VMs for NVIDIA GPUs). Ensure the NVIDIA Device Plugin is enabled (Azure provides an option to enable GPU support which sets this up). Use Azure Container Registry (ACR) for storing your model images close to the cluster (and enable geo-replication if needed to avoid egress between regions). For data, Azure Blob Storage or Azure Files can serve as places to keep model checkpoints; Azure Files can be mounted in AKS as a volume if you need a shared filesystem (though performance might not be as high as an HPC filesystem). AKS integrates with Azure Monitor for logging and metrics – you can set up container insights to watch GPU metrics. For autoscaling, use the Cluster Autoscaler on AKS, or Azure’s managed option if available, to scale VM pools. Azure also has spot VM scale sets – you can add a node pool of spot GPUs to AKS and label it for certain deployments. One consideration on Azure is to use Availability Sets or Zones for HA – spread your LLM pods across zones to protect against a zone failure, but be mindful of cross-zone latency for distributed training (for pure inference it’s usually fine). Azure’s networking (like Application Gateway or Front Door) can be used as an ingress in front of AKS to provide SSL termination, WAF, and global routing.

  • Self-Hosted Kubernetes: On-premises or self-managed clusters give you full control, but you’ll need to handle more details. Ensure GPU nodes have the NVIDIA drivers and Kubernetes device plugin configured. You might use the NVIDIA GPU Operator which simplifies deploying drivers, the device plugin, and monitoring for GPUs on your own cluster. Without cloud autoscalers, you’ll have to plan capacity – perhaps use cluster scheduling to ensure jobs don’t overwhelm resources. If running on bare metal, consider using MetalLB for service LoadBalancers or an ingress controller like NGINX or Traefik for exposure. Storage for models might be an NFS server or CephFS in your data center – ensure high throughput if you load models on startup. Networking should be high-bandwidth, low-latency especially if you do multi-node model sharding (maybe use InfiniBand or RoCE networking for GPU clusters if available). Also, implement backup and disaster recovery: if a node dies, do you have mechanisms to quickly reschedule the LLM on another node with the required GPU and have access to the model data? Using StatefulSets with persistent volume claims can help ensure a new pod attaches the same storage. Lastly, monitor power and temperature for on-prem GPU servers – LLM workloads can be intense, so proper cooling and fail-safes (nvidia-smi can be used to throttle or shut off on overheat) are important to avoid hardware damage.

By adhering to these best practices and utilizing the right tools, you can reliably serve open-source LLMs on Kubernetes with high performance while maintaining security and controlling costs. Kubernetes provides the flexibility to scale from small deployments to serving massive billion-parameter models across clusters (Scaling your LLM inference workloads: multi-node deployment with TensorRT-LLM and Triton on Amazon EKS | AWS HPC Blog), all while leveraging a vibrant ecosystem of open-source software for orchestration, monitoring, and optimization. With thoughtful design and automation, deploying LLMs on Kubernetes can empower your applications with powerful AI capabilities in a robust, production-grade manner.