Deploy Nvidia NIM for LLM on Kubernetes

An NVIDIA NIM (NVIDIA Inference Microservice) for LLMs is a containerized, production-ready microservice that wraps a pre-trained and optimized large language model (LLM) with standardized APIs and an inference engine for easy deployment, scaling, and integration into applications. It abstracts away the complexity of model serving, optimization, and infrastructure, allowing you to focus on building intelligent features without needing to build the inference microservice.

For production-scale applications, NVIDIA NIMs can be deployed on Kubernetes clusters, enabling:

• Scalable Inference: Automatically scale NIM replicas based on traffic and resource availability.
• GPU Scheduling: Leverage Kubernetes with GPU-aware scheduling to optimize performance.
• Service Discovery & Load Balancing: Integrate NIMs into a microservices architecture with built-in support for routing and failover.
• CI/CD Integration: Deploy updates and manage lifecycle using DevOps pipelines.

This approach is ideal for teams building enterprise-grade AI services, as it combines the flexibility of containerized inference with the robustness of Kubernetes orchestration.

This lab provides the learner with hands-on experience deploying an NVIDIA NIM for LLM on Kubernetes.  The key topics covered in this lab include:

• Understand system requirements
• Understand the GPU Operator
• Deploy the NIM Operator
• Deploy the NIM Cache
• Deploy the NIM Service
• Expose the NIM microservice external
• Test accessing the NVIDIA NIM for LLM microservice

• Ubuntu
• NVIDIA A100 GPU
• Kubernetes
• NVIDIA GPU Operator
• NVIDIA NIM Operator
• NVIDIA NIM for LLM