Accelerated Computing Infrastructure

📅Published 2026-04-17·🔄Updated 2026-07-19·⏱4 min read

The layer that determines which accelerated instances · how to schedule · and which driver/partitioning stack to manage on Kubernetes. It covers not only NVIDIA GPUs but also AWS custom accelerators (Trainium/Inferentia). This layer must be established for upper-layer inference frameworks (vLLM, llm-d, etc.) to run stably.

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EKS GPU Node Strategy

Auto Mode vs Karpenter vs Managed Node Group vs Hybrid Node — optimal node selection by workload, security hardening, troubleshooting.

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GPU Resource Management

Karpenter NodePool, KEDA scaling, DRA dynamic resource allocation, Spot/Consolidation cost optimization strategies.

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NVIDIA GPU Stack

GPU Operator ClusterPolicy, DCGM monitoring, MIG·Time-Slicing partitioning, Dynamo inference framework.

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AWS Neuron Stack

Trainium2/Inferentia2, Neuron SDK 2.x, aws-neuron-device-plugin, NxD Inference, vLLM Neuron backend.

Selection Guide

If focused on NVIDIA, read Node Strategy → Resource Management → NVIDIA Stack; if considering AWS silicon (Trainium/Inferentia), read Node Strategy → Neuron Stack.