AI Software Infrastructure

Turn a GPU Server into a Production AI Platform

When you purchase a Broadberry GPU system, it can be delivered with a complete NVIDIA and Ubuntu software stack, including GPU drivers, AI runtimes, management tools, and a security-maintained operating system foundation. The result is a validated platform designed for production AI deployments rather than a collection of components that must be integrated and maintained separately.

Scaling Beyond a Single Server

When your AI workload grows beyond a single server, you need more than just extra GPUs. You need a platform that stays consistent, scales cleanly, and is easy to operate.

Broadberry designs multi-node GPU clusters for AI training, inference, and AI services, with integrated networking, software management, orchestration, and infrastructure consistency built into the overall solution.

What is AI Software?

AI software infrastructure is the collection of operating systems, drivers, runtimes, AI frameworks, orchestration tools, and management platforms that enable AI workloads to run reliably in production.

A complete AI software stack typically includes:

• GPU drivers and runtime environments
• AI frameworks and model services
• Containerisation and orchestration platforms
• Security and operating system maintenance
• Cluster management and resource scheduling

AI software environments commonly support frameworks such as PyTorch, TensorFlow, JAX, and other machine learning and generative AI tools.

Performance, reliability, and scalability depend on how software, hardware, storage, and networking operate together as a unified system.

NVIDIA AI Enterprise

AI Software for Development, Training, and Production AI

Broadberry provides licensing and access to NVIDIA AI Enterprise, a secure, cloud-native software platform designed to accelerate AI development and simplify deployment of production AI applications.

The platform includes AI frameworks, inference services, cluster management tools, GPU orchestration capabilities, and infrastructure software designed to support enterprise AI environments.

Ubuntu Pro (enterprise security and lifecycle)

Ubuntu Pro provides the operating system foundation for production AI environments, delivering extended security maintenance, long-term platform stability, and enterprise lifecycle support.

Ubuntu Pro provides a stable and supportable foundation for AI infrastructure operating at scale.

A Validated AI Software Stack

Together, NVIDIA AI Enterprise and Ubuntu Pro provide a validated software foundation for production AI environments. Organisations benefit from optimized GPU software, AI runtimes, enterprise operating system support, and software components designed to work together.

The result is reduced deployment complexity, faster time to production, improved software consistency, and a more scalable foundation for future growth.

Why Broadberry?

Broadberry is an NVIDIA Partner Network member with DGX partner status and extensive experience deploying AI infrastructure for enterprise, research, and HPC environments.

Broadberry works with customers to determine the most appropriate AI software architecture based on workload requirements, operational goals, security requirements, and deployment scale.

What sets Broadberry apart:

• Expertise across AI hardware and software infrastructure
• NVIDIA-aligned deployment best practices
• Validated AI software stacks
• Integration of compute, storage, and networking
• Support for AI clusters, AI PODs, and AI factories
• Guidance on selecting the appropriate AI software platforms based on workload requirements

AI Software FAQ

What is NVIDIA AI Enterprise?

NVIDIA AI Enterprise is a software platform that provides AI frameworks, microservices, runtimes, and deployment tools for production AI environments.

What is Ubuntu Pro?

Ubuntu Pro is an enterprise version of Ubuntu that provides extended security maintenance, compliance support, and long-term lifecycle management.

Do AI servers require specialised software?

Yes. AI servers typically require GPU drivers, AI frameworks, container runtimes, operating system support, and deployment tools that are optimised for AI workloads.

What software is required for AI training?

AI training environments commonly use GPU drivers, AI frameworks such as PyTorch and TensorFlow, container platforms, orchestration tools, and cluster management software.

What software is required for AI inference?

Inference environments typically use model serving platforms, AI runtimes, microservices, and deployment frameworks designed to deliver low-latency predictions.

When should Kubernetes be used for AI?

Kubernetes is often adopted when AI workloads scale across multiple servers or when organisations need repeatable deployments, resource management, and operational consistency.

How do you choose the right AI software stack?

The right software stack depends on workload requirements, deployment scale, security needs, operational complexity, and long-term support requirements. Broadberry works with customers to evaluate these factors and recommend the most appropriate AI software architecture.