As organizations scale AI workloads in production, GPU orchestration on Kubernetes introduces unique challenges that can significantly impact performance and costs.
This comprehensive guide covers real-world solutions for:
• Reducing GPU pod startup times from 8+ minutes to under 2 minutes through image caching, streaming, and node optimization strategies
• Building multi-tier fallback architectures with LiteLLM and OpenRouter for 99.9% uptime when self-hosted models fail
• Maximizing GPU utilization beyond 70% using time-slicing, MIG partitioning, and intelligent batching
• Production-ready monitoring, cost controls, and spot instance handling
Includes practical YAML configs, optimization techniques, and a complete production checklist based on scaling GPU infrastructure at KubeAce.
Whether you're running LLM inference, model training, or AI applications, these battle-tested strategies will help you build resilient, cost-effective GPU infrastructure.
HarishSush•2h ago
This comprehensive guide covers real-world solutions for:
• Reducing GPU pod startup times from 8+ minutes to under 2 minutes through image caching, streaming, and node optimization strategies • Building multi-tier fallback architectures with LiteLLM and OpenRouter for 99.9% uptime when self-hosted models fail • Maximizing GPU utilization beyond 70% using time-slicing, MIG partitioning, and intelligent batching • Production-ready monitoring, cost controls, and spot instance handling
Includes practical YAML configs, optimization techniques, and a complete production checklist based on scaling GPU infrastructure at KubeAce.
Whether you're running LLM inference, model training, or AI applications, these battle-tested strategies will help you build resilient, cost-effective GPU infrastructure.