AI & GPU Workloads
3 articles from official documentation
Accelerate AI Model Distribution with Dragonfly's P2P Magic
Tired of slow model downloads? Dragonfly’s peer-to-peer acceleration can reduce your origin traffic by 99.5%. Discover how it splits files and shares them across nodes for lightning-fast distribution.
- →Leverage P2P to reduce model download times dramatically.
- →Configure repository types to optimize your downloads.
Deploying Generative AI at the Edge with EKS Hybrid Nodes and NVIDIA DGX
Unlock the power of generative AI at the edge with Amazon EKS Hybrid Nodes and NVIDIA DGX. This setup allows you to connect on-premises infrastructure directly to the EKS control plane, ensuring low-latency AI services. Learn how to configure your environment for optimal performance.
- →Connect on-premises infrastructure to Amazon EKS using EKS Hybrid Nodes for low-latency AI services.
- →Deploy NVIDIA DGX Spark as a hybrid node to optimize edge AI deployment.
Unlocking AI Workloads: The AI Gateway Working Group Explained
The AI Gateway Working Group is set to revolutionize how we handle AI workloads in Kubernetes. With proposals like payload processing and egress gateways, it addresses critical needs for inspecting and transforming HTTP payloads. Dive in to understand its impact on your infrastructure.
- →Understand the AI Gateway as a specialized infrastructure for AI workloads.
- →Leverage the payload processing proposal to inspect and transform HTTP payloads.
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