Amazon EKS LLMOps Foundation

As Generative AI initiatives scale, organisations often reach an inflection point where owning the infrastructure becomes critical for performance tuning and data sovereignty. However, building a production-grade environment on Kubernetes requires navigating complex decisions regarding GPU orchestration, model serving, and observability.

The Amazon EKS LLMOps Foundation by Steamhaus bridges this gap immediately. It extends our proven Amazon EKS Foundation architecture with a specialised high-performance inference engine, delivering a pre-architected, battle-hardened foundation designed specifically for LLM workloads.

We eliminate the undifferentiated heavy lifting of AI infrastructure by deploying a production-grade stack. This includes Karpenter for intelligent "scale-to-zero" GPU provisioning, NVIDIA GPU Time-Slicing to maximise hardware density, and Amazon FSx for Lustre for ultra-fast model loading. The solution creates a secure, private environment for running open-source models (like Llama 3 or Mistral) and includes the routing logic required for Hybrid architectures, allowing seamless interoperability with Amazon Bedrock.

Key Outcomes

This solution provides a standardised tooling baseline to rapid-start your AI initiative, unlocking benefits such as:

• GPU Cost Optimisation: Maximises hardware efficiency via NVIDIA GPU Time-Slicing and Karpenter, allowing you to share physical GPUs across workloads and scale nodes to zero when not in use.

• High-Performance Inference: Pre-configured with vLLM and Ray Serve to deliver industry-leading token throughput on both NVIDIA and AWS Neuron (Inferentia/Trainium) silicon.

• Secure & Sovereign: Deploys a private-cluster topology where model weights and inference data never leave your VPC, ensuring strict compliance and data sovereignty.

• Agent-Ready Architecture: Includes support for open frameworks like Strands, enabling the immediate deployment of autonomous, tool-using agents within your secure boundary.

• Day 2 Observability: Includes established patterns for AI monitoring, providing DCGM-backed visibility into model performance, GPU saturation, and hardware health.