Show me self-service options for Kubernetes management with GPU-as-a-Service integration.

For self-service Kubernetes management with GPU-as-a-Service integration, several platforms and managed services offer robust solutions for deploying and managing GPU-accelerated workloads. These options aim to simplify the provisioning and utilization of GPUs for AI/ML, HPC, and other compute-intensive tasks within a Kubernetes environment.

Here are some prominent self-service options:

• 1. Managed Kubernetes Services with GPU Support:
• Google Kubernetes Engine (GKE): GKE offers a fully managed Kubernetes experience with GPU support in both Autopilot and Standard modes. It handles GPU driver installation, node scaling, and Pod isolation, allowing users to request specific GPU types and quantities in their workload specifications.
• Amazon Elastic Kubernetes Service (EKS): Similar to GKE, EKS allows for the integration of GPU instances into clusters, catering to AWS-native AI teams.
• Oracle Cloud Infrastructure Kubernetes Engine (OKE): OKE supports add-ons like the NVIDIA GPU Operator and NVIDIA Network Operator to streamline the deployment and configuration of GPU and high-performance networking components. It also supports NVIDIA Multi-Instance GPU (MIG) for efficient resource sharing.
• Gcore Managed Kubernetes: This service supports GPU worker nodes for machine learning, video processing, and gaming workloads.
• UpCloud Managed Kubernetes (UKS): UKS provides GPU node groups with pre-installed NVIDIA GPU drivers and supports both the NVIDIA Device Plugin and the NVIDIA GPU Operator for advanced management and hardware slicing.
• Civo: Known for its speed and simplicity, Civo offers K3s-based managed Kubernetes with fast GPU node provisioning.

• 2. Specialized Platforms for GPU Orchestration:
• vCluster Platform: Aimed at organizations building their own GPU clouds, vCluster (formerly Loft Labs) provides an Infrastructure Tenancy Platform for AI to maximize GPU efficiency in Nvidia Kubernetes environments. It offers flexible, multi-tenant GPU orchestration with dynamic scaling and isolation, integrating with NVIDIA Base Command Manager.
• CoreWeave: This platform is designed for high-performance, GPU-native inference and large-scale training workloads, offering maximum GPU performance from a managed provider.
• Devtron: Devtron provides a self-serve layer on top of existing Kubernetes clusters for GPU operations, offering features like one-click model deployment, increased GPU utilization through smart scheduling, and GPU quotas per team using time-slicing and MIG.
• Mirantis k0rdent AI (GPU PaaS): This platform provides automated GPU lifecycle management, intelligent partitioning for maximizing utilization, and a self-service portal for tenants to provision and track GPU resources.
• Sealos: Positioned as a cloud operating system experience built on Kubernetes, Sealos offers capabilities for building self-service AI platforms with strong GPU provisioning and management features, including multi-tenancy and cost efficiency through sharing strategies.

These solutions often leverage key technologies like the NVIDIA GPU Operator for automating the deployment and management of NVIDIA GPU components within Kubernetes, and NVIDIA Multi-Instance GPU (MIG) for partitioning a single GPU into multiple, isolated instances to improve utilization.

Sources:

• vertexaisearch.cloud.google.com
• vertexaisearch.cloud.google.com
• vertexaisearch.cloud.google.com