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2024-08-15Hünkar Döner

AWS Architecture for Generative AI Applications: Bedrock, EKS, GPU Nodes

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AWS Architecture for Generative AI Applications

Generative AI is sweeping the business world. It is revolutionizing every field from chatbots to image generation, from coding to data analysis. However, you need the right infrastructure to implement this revolution in your own company. AWS offers a wide range for GenAI projects, from "ready-made services" to "manage your own infrastructure" models.

In this article, we will examine three popular AWS architectural components for GenAI workloads: Amazon Bedrock, Amazon EKS, and GPU Nodes.

1. Amazon Bedrock: Fastest Start

If your goal is not to train a model from scratch but to quickly develop applications using the best existing models (Foundation Models), Amazon Bedrock is tailor-made for you.

  • What is it? A fully managed service that provides access to models from AI21 Labs, Anthropic, Cohere, Meta, and Amazon's own models via a single API.
  • Advantage: No server management (Serverless). You don't deal with infrastructure. Your data is not used for model training, it is secure.
  • Use Case: Customer service bots, text summarization, RAG (Retrieval-Augmented Generation) applications.

2. Amazon EKS and GPU Nodes: Full Control and Customization

If you want to fine-tune an open-source model (Llama 2, Mistral, etc.) with your own data or train your own model completely, you need more control and power.

  • Amazon EKS (Elastic Kubernetes Service): The industry standard for running and scaling AI workloads in containers. You can optimize your EKS setup with our Kubernetes Consultancy service.
  • EC2 GPU Instances: High processing power is required for model training and inference. You can use AWS's P4, P5 (NVIDIA A100/H100) or more cost-effective g5 series instances as worker nodes on EKS.
  • Scaling with Karpenter: GPU servers are expensive. By using Karpenter on EKS, you can achieve massive cost savings by turning on GPU servers only when the training job starts and turning them off when it finishes.

3. Data Storage and Vector Databases

GenAI applications need Vector Databases to have memory.

  • Amazon OpenSearch Service (Serverless): Located at the heart of RAG architectures with its vector search capability.
  • Amazon Aurora (pgvector): If you like PostgreSQL, you can use your Aurora database to store vectors.

Which Path to Choose?

  • Speed and Ease: Amazon Bedrock.
  • Control and Customization: Amazon EKS + EC2 GPU.
  • Cost Focused: Spot GPU usage on EKS or AWS Trainium/Inferentia chips.

Establishing the right architecture in your AI journey is critical for project success. You can get support from our AWS Consultancy team to design the GenAI infrastructure that best suits your needs.