IT teams that need one place to build, tune and run AI models can use Nvidia AI & GPU Servers to keep development and production on a standard platform. The range covers current enterprise AI systems, from balanced training and inference nodes to large-memory options for models that outgrow conventional GPU servers.
In data centres and shared AI environments, these systems support controlled roll-out, simpler procurement and less platform sprawl. They help keep workloads responsive, reduce memory bottlenecks and give teams a clearer path from experimentation to ongoing AI service delivery.
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| Model | Popularity | Deployment | Primary Use Case | Form Factor | CPU Vendor | Processor Platform | Maximum CPUs Supported | Maximum Memory Capacity | Memory Slots | Maximum GPUs Supported | |
|---|---|---|---|---|---|---|---|---|---|---|---|
Nvidia DGX B200 AI System
|
★ ★ ★ | HPC / AI | AI Training & Inference | 10U | Intel | Xeon Scalable 5th Gen | 2 | 4 TB | 32 | 8 | View |
Nvidia DGX GH200 AI System
|
★ ★ ★ | AI Factory / HPC | Large-Scale AI Training | 10U | NVIDIA | Grace Hopper Superchip | 1 | 480 GB | Integrated | 256 | View |
Nvidia DGX H100 AI System
|
★ ★ ★ | HPC / AI | AI Training & Inference | 8U | Intel | Xeon Scalable 4th Gen | 2 | 4 TB | 32 | 8 | View |
Data centre teams need a current AI platform that can support training, fine-tuning and inference on one system, without moving into more specialised giant-memory designs. Nvidia AI & GPU Servers help simplify platform choices for enterprise AI operations.
Studios and generative-content teams need a standardised AI appliance for model development, asset generation and inference. Nvidia AI & GPU Servers provide a practical way to move from experimentation to day-to-day production work.
Hospitals, pharma and research groups need one system that can support imaging, genomics and clinical AI across both development and deployment. Nvidia AI & GPU Servers help teams operationalise demanding workloads on a single platform.
Finance teams need a balanced platform for model building, risk analysis and inference, especially when they want a standard DGX operating model. Nvidia AI & GPU Servers support both experimentation and production use.
Platform and ML teams need a current DGX reference system for building, tuning and deploying production AI workflows. Nvidia AI & GPU Servers give them a develop-to-deploy base that fits the standard enterprise AI stack.
Backed by decades of expertise in the IT sector, our specialists support every stage of your deployment — from initial selection through to long-term lifecycle management.
Standard enterprise servers can support smaller AI projects, but limitations usually appear once training, inference and large-scale data processing begin demanding more GPU performance, memory bandwidth and faster system connectivity.
Nvidia AI & GPU Servers are designed for AI and accelerated computing workloads, helping IT teams run training, inference, simulation and analytics more efficiently across enterprise and research environments.
Very large shared-memory systems can solve specialised AI and HPC challenges, but not every environment needs that level of scale or deployment complexity.
DGX B200 is built for enterprise AI training and inference in a standard DGX platform, making it a better fit where teams need current-generation AI performance without moving into the large shared-memory design of DGX GH200.
Conventional GPU systems can become harder to manage once models and datasets grow large enough that workloads must constantly be split across multiple servers.
DGX GH200 is designed for memory-intensive AI and HPC workloads, including large language models, graph analytics and recommender systems. Its large shared-memory architecture helps teams work with bigger datasets and models more efficiently while reducing workflow complexity.
Moving directly to the newest platform is not always necessary, especially where IT teams already need a proven AI platform with established deployment patterns and broad software support.
DGX H100 remains widely used for enterprise AI training and inference because it combines strong generative AI performance with a mature DGX ecosystem. For many environments, that creates a more predictable deployment and scaling path than moving immediately into Blackwell-based or large shared-memory platforms.
General-purpose infrastructure can struggle once imaging, genomics and AI research workloads begin processing very large datasets across multiple departments and projects.
Nvidia AI & GPU Servers help healthcare and research teams accelerate training, simulation and inference workloads on dedicated GPU infrastructure. This helps reduce processing time while supporting larger-scale AI and research environments more effectively.
Adding more CPU servers can support gradual workload growth, but AI training and accelerated analytics often reach a point where CPU-only environments become harder to scale efficiently.
Nvidia AI & GPU Servers provide dedicated GPU acceleration for AI and high-performance computing workloads, helping teams process larger models more efficiently while reducing pressure on CPU resources, rack space and overall infrastructure complexity.
If these options aren’t the right fit for your environment, we provide a wide portfolio of product series and solutions that may better suit your infrastructure. Explore below, or speak to our team and we’ll help you find the right match.
Whether you know exactly what you need or you’re still evaluating options, our team is available for a no-obligation conversation.
