Business Use Case:

Enterprises looking to harness the power of large language models (LLMs) for applications like customer service automation, content creation, and data analysis can benefit from deploying NVIDIA NIM LLM on EKS. This setup provides high performance, scalability, and cost-efficiency, enabling businesses to improve service quality and operational efficiency.

Overview:

This guide covers deploying NVIDIA NIM LLM on Amazon EKS, leveraging NVIDIA’s GPU acceleration for optimal performance. It details the integration with Karpenter for dynamic scaling and Amazon EFS for shared storage, automated through Terraform.

Detailed Steps:

1. Infrastructure Provisioning:

• EKS Cluster: Set up an Amazon EKS cluster optimized for GPU workloads, necessary for handling the computational demands of LLMs.

• Terraform Automation: Use Terraform to automate the provisioning of the required infrastructure, ensuring a consistent and repeatable deployment process.

2. Dynamic Scaling with Karpenter:

Karpenter: Implement Karpenter to dynamically scale the infrastructure at the instance level. This ensures resources are allocated based on real-time demands, optimizing costs and ensuring high availability.

3. High-Performance Storage with Amazon EFS:

Amazon EFS: Deploy Amazon EFS for scalable, shared storage solutions, facilitating efficient data access and management across the cluster.

Business Value:

• Cost Efficiency: Dynamic scaling ensures resources are used only as needed, reducing operational costs.

• High Performance: GPU acceleration and optimized infrastructure provide rapid processing of language models, improving application performance.

• Scalability and Flexibility: Easily scales to meet growing demands, supporting business expansion and innovation.