Vultr Enables Enterprise AI with Software Components from AMD

Open Building Blocks for Production AI at Scale

At a Glance: 

• Vultr makes AMD Enterprise AI software components, optimized for AMD Instinct™ GPUs, available on the Vultr Marketplace through a close collaboration between AMD Silo AI and Vultr. 
• Running on Vultr's global cloud GPU infrastructure with managed Kubernetes, the component set includes AMD Inference Microservices (AIMs), AMD AI Workbench, and AMD Resource Manager. 
• Deploying the AMD AI Workbench from Vultr's Marketplace eliminates the complexity of traditional setups and gives customers access straight away to a production grade environment for AI development

Vultr, one of the largest privately-held cloud infrastructure providers, is bringing enterprise AI software components from AMD to its marketplace. Built by AMD Silo AI and powered by AMD Instinct™ GPUs, the offering runs on Vultr's global cloud GPU infrastructure with managed Kubernetes. It includes a catalog of AMD Inference Microservices (AIMs) and the AMD Resource Manager, deployable through the AMD AI Workbench and available on the Vultr Marketplace today. 

Cluster, networking, and certificate setup are handled out of the box, enabling users to move straight from provisioning to development.

“Vultr and AMD are helping enterprises operationalize AI inference with a standardized, open microservices architecture optimized for AMD Instinct GPUs. Enterprise teams can deploy secure, scalable inference services globally in minutes while maintaining flexibility across existing infrastructure, reducing operational overhead, improving GPU utilization, and accelerating the transition from AI development to production deployment," says Kevin Cochrane, Chief Marketing Officer at Vultr.

For platform teams evaluating the Vultr+AMD stack, the experience starts at the Marketplace and results in a secured inference endpoint in a matter of minutes, eliminating the manual integration work that usually sits in between.  

“Deploying the AMD AI Workbench on Vultr's managed Kubernetes eliminates the complexity of traditional setups, no VKE configuration required, which dramatically reduces our deployment time. What truly sets this marketplace solution apart is its out-of-the-box capabilities. When you deploy an AMD inference microservice from the AI Workbench, you automatically get public IP assignment and SSL certificate provisioning for secure communication. These aren't add-ons or afterthoughts, they're built into the foundation. That's the kind of streamlined experience our customers deserve", says Mayank Debnath, Director of Developer Relations at Vultr.

Enterprise AI is Moving Beyond Experimentation

The enterprise AI landscape has shifted decisively. Organizations are no longer asking whether to deploy AI, they are asking how to do it at scale, under their own terms, without surrendering control to a single vendor’s ecosystem. 

Yet the path from prototype to production for enterprise and agentic solutions remains fractured. Development teams stitch together inference engines, orchestrators, model registries, and monitoring tools from dozens of sources, each with its own requirements, licensing constraints, and integration overhead. The fragmented environment results in brittle stacks, unpredictable costs, and architectures that resist adaptation as models and requirements evolve. 

The challenge is consistent across the ecosystem. Whether deploying on‑premises, building customer solutions, embedding AI into platforms, or offering managed services, organizations need the same thing: production ready AI infrastructure that is flexible, open, and reliable at scale. 

"Our approach to enterprise AI software is built around a simple idea: enterprise AI infrastructure should be modular, open, and adaptable. Instead of forcing organizations into a monolithic platform, we enable customers to use composable building blocks that integrate with existing environments and support the full AI lifecycle, from bare metal to production inference, including fine-tuning, scalability, security and governance,” says Alexander Finn, Senior Director, AMD Silo AI.

Software Engineered for the Enterprise AI Lifecycle

The enterprise AI software components from AMD are purpose-built to answer a straightforward question: what if enterprise AI infrastructure would be built as composable open microservices - building blocks that organizations adopt, adapt, and extend to complement the software stacks they already run?  

The answer is not a monolithic platform, but a composable ecosystem of modular components, each independently deployable and purpose-built for a specific function in the AI lifecycle. All components are available through a permissive open-source license, meaning no license fees and freedom to modify and commercialize. 

Spanning the full AI lifecycle, from inference serving and GPU governance to workload orchestration and application templates, each of the four core components ship with dedicated features for running AI at production scale.

AMD AI Workbench

The AMD AI Workbench is a comprehensive development environment providing self-service GPU-enabled workspaces (VSCode and JupyterLab), a catalog of optimized AIMs, and reference workloads for training and fine-tuning. The Workbench includes the AIM Engine, a Kubernetes operator that orchestrates the full lifecycle of AIMs on your cluster, including configurable inference autoscaling.

AMD Inference Microservices (AIMs) 

AIMs are containerized, production-ready inference microservices that deliver standardized model serving with an OpenAI-compatible API. They provide automatic hardware detection and optimization for AMD hardware, intelligent profile-based configuration, and support for the broad ecosystem of open foundation models. Deploy any supported model with consistent APIs, automatic scaling, and enterprise-grade reliability, whether running a single endpoint or hundreds.

AMD Resource Manager

The AMD Resource Manager enables enterprise-grade GPU governance and AI workload orchestration. It delivers cluster administration, organization and team hierarchy management, GPU quota allocation and enforcement, role-based access control, and SSO/IAM integration. Its integrated scheduling engine, purpose-built for AI workloads, provides fair GPU resource sharing, and guaranteed quotas, minimizing GPU idleness while helping ensure workload isolation and predictability, paired with real-time monitoring dashboards providing visibility at every level.

AMD Solution Blueprints

The AMD Solution Blueprints are a catalog of reference applications that combine AIMs with orchestration logic to solve real-world enterprise and agentic challenges. With 15+ validated templates, including Agentic RAG, document summarization, code assistants, financial intelligence, and multi-agent workflows, AMD Solution Blueprints accelerate time-to-value by providing deployable architectures that teams can customize for their specific requirements.

Built for the Ecosystem: On-Prem, Integrators, ISVs, OEMs, and CSPs

The permissive open-source licensing model and modular architecture make the AMD components uniquely suited to serve the breadth of the enterprise AI ecosystem, offering various advantages depending on context and use case.

On-Premises Enterprises: provide the freedom to customize every component, integrate with existing security and governance frameworks, and scale without per-node licensing concerns. 

System Integrators: leverage the components as composable building blocks for customer deployments by selecting the AIMs that match individual engagements and combining them with proprietary value-add services, delivering turnkey AI solutions without reinventing infrastructure. 

Independent Software Vendors (ISVs): accelerate AI-powered product development by embedding AIMs directly into their software products and vertical applications. The permissive open-source license reduces redistribution barriers, enabling ISVs to bundle, ship, and monetize AI capabilities as part of their own offerings.

Original Equipment Manufacturers (OEMs): embed AMD Solution Blueprints and individual microservices or the complete stack into their platforms and appliances. The permissive open-source license enables white-label distribution, while the modular architecture helps to ensure clean integration boundaries.

Cloud Service Providers (CSPs): build differentiated managed AI services on top of the stack. Deploy the full ecosystem or individual components as integrated parts of the platform offering.

Open by Design: Yours to Run, Modify, and Ship

Every component, from AIMs through AMD AI Workbench and Solution Blueprints, ships under a permissive open-source license. This means: 

• No licensing fees, deploy at any scale without per-node, per-GPU, or per-token charges 
• Full modification rights, adapt any component to your specific requirements 
• Redistribution freedom, embed, bundle, and commercialize without copyleft obligations 
• Vendor choice, the components are built on ROCm™ and Kubernetes and integrates with existing toolchains  

The software stack was designed this way for a reason. Success in enterprise AI requires infrastructure that the ecosystem can build on, extend and deploy across hardware. Enterprises benefit from the deployment flexibility and vendor independence that open standards and frameworks offer.

Available Now: Try It on Vultr Marketplace

The AMD Inference Microservices catalog and the AMD Resource Manager are available today through the AMD AI Workbench on the Vultr Marketplace, delivering an accelerated path for enterprises to go from zero to production-grade AI inference. 

Get started today:  

• Explore the AMD AI Workbench and the rest of the AIM ecosystem on Vultr Marketplace at discover.vultr.com/amd-ai-workbench
• Review the documentation at enterprise-ai.docs.amd.com
• Access the source on GitHub at github.com/amd-enterprise-ai

Vultr’s global cloud GPU infrastructure, combined with AMD Instinct GPUs and the pre-configured components, enables AI application builders and organizations to deploy AI services in minutes rather than months. Whether evaluating the stack for future on-premises deployment or running production workloads in the cloud, the Vultr Marketplace provides an immediate starting point. 

The future of enterprise AI is open, modular, and built for the organizations that deploy it. The enterprise AI software components from AMD deliver the building blocks to make that future a reality.