# Accelerate AI/ML Workloads with Unified AI Infrastructure

Most organizations have invested in AI infrastructure, but struggle to run AI/ML workloads at scale. Environments are fragmented, provisioning is manual, and orchestration across GPUs, Kubernetes, and cloud platforms is inconsistent. As a result, platform teams spend more time managing systems than enabling teams to build and deploy AI.

Rafay solves this with a unified approach to AI orchestration, enabling platform teams to standardize environments, automate lifecycle management, and deliver self-service access to AI/ML workloads across cloud, data center, and edge.

## AI application delivery has never been easier.

While many GPUs are underutilized, The Rafay Platform stack ensures AI application delivery is faster, more accurate, and more secure than ever – giving companies the competitive edge they need to take hold of evolving GenAI initiatives in the business.

Whether a GPU cloud or sovereign cloud provider, The Rafay Platform supports national data sovereignty, residency, and compliance requirements so teams can worry less about infrastructure and focus their energy on innovation.

## Run AI/ML Workloads at Scale

Enable developers and data scientists to run AI/ML workloads without infrastructure friction. Rafay provides pre-configured environments and automated provisioning so teams can move from experiment to production faster—without waiting on manual setup or tickets.

## Orchestrate AI Infrastructure Across Environments

Rafay delivers centralized **AI orchestration** across Kubernetes clusters, GPUs, and hybrid infrastructure. Platform teams can manage workloads consistently across AWS, Azure, GCP, on-prem data centers, and edge environments from a single control plane.

## Standardize and Govern AI Workloads

Ensure every AI/ML workload runs in a secure, compliant, and repeatable environment. Rafay enforces policies, role-based access control, and multi-tenant isolation across all AI infrastructure—so teams can scale without introducing risk.

## Optimize AI Infrastructure Utilization

Maximize the value of your AI infrastructure by improving visibility and utilization of GPUs and compute resources. Rafay provides real-time insights into workload usage, helping teams reduce waste and align infrastructure consumption with business priorities.

## Focus on AI innovation, not infrastructure

The Rafay Platform stack helps platform teams manage AI initiatives across any environment – helping companies realize the following benefits:

### Harness the Power of AI Faster

Complex processes and steep learning curves shouldn’t prevent developers and data scientists from building AI applications. A turnkey MLOps toolset with support for both traditional and GenAI (aka LLM-based) models allows them to be more productive without worrying about infrastructure details.

### Reduce the Cost of AI

By utilizing GPU resources more efficiently with capabilities such as GPU matchmaking, virtualization, and time-slicing, enterprises reduce the overall infrastructure cost of AI development, testing, and serving in production.

### Increase Productivity for Data Scientists

Provide data scientists and developers with a unified, consistent interface for all of the MLOps and LLMOps work regardless of the underlying infrastructure, simplifying training, development, and operational processes.

## Start a conversation with Rafay

Talk with Rafay experts to assess your infrastructure, explore your use cases, and see how teams like yours operationalize AI/ML and cloud-native initiatives with self-service and governance built in.