Introduction

Explore various use cases and deployment scenarios for Armada Bridge for NCPs.

Large-Scale LLM Training and Large Scale Inference: Organizations that require massive compute power for training Large Language Models (LLMs) and executing high-performance inference workloads need direct access to GPU resources with minimal latency. The RA enables bare-metal GPU provisioning, ensuring dedicated hardware access for AI model pre-training, fine-tuning, distilling, and large-scale deployment.

AI Workloads in Virtualized Environments: Enterprises seeking flexible, cost-effective AI/ML computing require on-demand access to GPUs without the overhead of dedicated hardware. The RA supports virtualized GPU instances, allowing AI developers to run LLM inference, deep learning (DL) training, and AI-driven analytics in a scalable and isolated VM environment.

Enterprise AI Development on PaaS: Businesses looking to streamline their AI workflows often require an end-to-end AI development platform with built-in orchestration and automation. The RA enables Platform-as-a-Service (PaaS) capabilities, integrating Kubernetes-based solutions, job scheduling, and AI pipeline execution, allowing enterprises to focus on model development without managing infrastructure complexities.

Model Deployment and AI Services for End Customers: Companies providing AI-driven applications such as chatbots, image recognition, and real-time recommendation systems need a seamless way to deploy and serve models. The RA integrates with NVIDIA NIM, Hugging Face, and other model repositories, enabling efficient model serving and inference as a service.

Dynamic GPU Resource Allocation for On-Demand Workloads: Organizations with fluctuating AI/ML workload demands require scalable GPU allocation without over-provisioning. The RA enables dynamic capacity allocation via NVIDIA Cloud Functions (NVCF), allowing compute resources to be optimized in real-time based on workload requirements.

Multi-Tenant Infrastructure Slicing:

AI providers serving multiple customer segments with varied workload requirements must efficiently allocate resources while maintaining isolation. The RA supports infrastructure slicing, allowing organizations to provision bare-metal GPU resources, VMs for AI development, or managed PaaS environments from a single platform to enhance scalability and cost efficiency.