#gpu

5 key challenges faced when operating Agentic AI workloads

Guide to building Agentic AI platform using Amazon EKS and open-source ecosystem

In-depth technical documentation on the architecture, deployment, and operations of the Agentic AI Platform

Version-specific GPU checkpoint/restore constraints and an EKS graceful-drain and warm-start evidence procedure (Experimental)

Optimal node strategies for GPU workloads across EKS Auto Mode, Karpenter, MNG, and Hybrid Nodes

GPU resource management and cost optimization using Karpenter, KEDA, and DRA on EKS

EKS GPU node strategy, Karpenter·KEDA·DRA resource management, NVIDIA GPU stack, AWS Neuron stack — the accelerated computing layer covering GPUs and AWS custom accelerators

Architecture and EKS integration for GPU Operator, DCGM, MIG, Time-Slicing, and Dynamo

A guide to the GPU infrastructure, inference framework, and inference optimization layers, with a single map of the end-to-end LLM inference request path and per-layer tuning levers — inference gateway, prefill/decode disaggregation, KV cache-aware routing, LMCache, and cache-hit strategy.

llm-d architecture concepts, KV Cache-aware routing, Disaggregated Serving, EKS Auto Mode integration strategy

Architecture concepts, distributed deployment strategies, and performance optimization principles for Mixture of Experts models

vLLM PagedAttention, parallelization strategies, Multi-LoRA, and hardware support architecture

2-Tier GPU autoscaling (KEDA·Karpenter), DRA compatibility, and operational lessons learned from large MoE model (GLM-5·Kimi K2.5) deployments for LLM serving

EKS architecture overview for maximizing LLM Inference performance — starting point for vLLM, KV Cache-Aware Routing, Disaggregated Serving, LWS multi-node, and GPU autoscaling

Production deployment and configuration reference architecture for the Agentic AI Platform

Hands-on guide to deploying large open-source models on EKS, based on the GLM-5.1 experience

Official GPU, Trainium2 and Inferentia2 specifications, Llama 4 model requirements, and a plan for measuring performance and cost

Hardware requirements, four workload families, and a measurement plan for comparing aggregated and disaggregated NVIDIA Dynamo serving on EKS

Debugging guide for GPU/AI workloads on EKS

A framework guide covering the DRA core model (DeviceClass, ResourceClaim, ResourceSlice), resource types beyond GPUs (NICs, interconnects, FPGAs), and adoption criteria

Designs a highly available GenAI inference layer for EKS Hybrid Nodes GPU nodes with resource isolation (taints), a hybrid-only NVIDIA Device Plugin deployment, and a Karpenter-based cloud GPU fallback NodePool.

A hands-on guide to using on-premises GPU nodes as the primary inference tier on EKS Hybrid Nodes, and resolving DGX H200 SR-IOV VF name inconsistency through driver compatibility, persistent naming, and systemd orchestration

Covers GPU workload architecture for EKS Hybrid Nodes and DGX H200 SR-IOV and InfiniBand high-performance networking configuration.