AI/ML and GenAI
🧱 Core Platform¶
-
GPU PaaS
Convert a stack of GPUs into a dynamically partitioned, multi-tenant GPU Cloud for data scientists and GenAI developers.
Overview | Administration | End Users | GPU Cloud Providers | Get Started
-
GPU Sharing
Share your GPU resources with multiple users/applications.
Overview | Time Slicing | Nvidia MIG-Single | Nvidia MIG-Mixed | Get Started
🖥️ AI Infrastructure (Compute)¶
-
Bare Metal Servers
Instant self-service provisioning of GPU-enabled bare metal servers with a single click.
-
Virtual Machines
1-Click, self-service provisioning of GPU-enabled virtual machines.
Overview | Architecture | Requirements | Setup | Metrics | Common Configs | Videos
-
Managed Kubernetes
1-Click, self-service provisioning of GPU enabled Managed Kubernetes Clusters.
-
Virtual Clusters
1-Click, virtual clusters providing users with a lightweight, fully isolated virtual Kubernetes clusters
-
Serverless Pods
Deploy custom containers from prebuilt templates in isolated environments enabling users to efficiently run CPU and GPU-intensive tasks without managing physical infrastructure.
🛠️ AI/ML Tooling/Apps¶
-
Jupyter Notebooks
1-Click Jupyter Notebook based Interactive Development Environment with instant access to NVIDIA GPUs – start building in minutes.
-
Inferencing with Hourly Metering
Deploy and operate real-time AI inference for popular LLMs with support for hourly metering/billing.
-
Serverless Inference with Token based Metering
Deliver a fully managed serverless inference service for popular LLMs with support for token based metering/billing.
-
MLOps-Kubeflow
Deploy and operate a multi-tenant MLOps platform on your infrastructure based on Kubeflow, MLflow, TensorBoard etc.
-
MLOps-KubeRay
Provide your users with a Ray as a Service multi-tenant offering on your infrastructure.
-
SageMaker AI (AWS)
End User Self Service access to an AWS SageMaker profile with automated enterprise-grade governance and controls.