GPU platform
Discuss NVIDIA H100, H200 or A100 configurations with our team. The proposed model and configuration are subject to availability.
AI & GPU Servers
Accelerate Your AI Workloads.
Dedicated GPU servers and AI infrastructure for training, fine-tuning, inference and high-performance computing. Configure an environment built around your performance, memory and scalability requirements.
Dedicated GPU Servers
NVIDIA GPU Platforms
Flexible Configurations
Expert Support
Built for AI Workloads
GPU Infrastructure Designed Around Your Workloads
From large language models and generative AI to computer vision, inference and HPC, we configure GPU infrastructure around your compute, GPU memory, storage and networking requirements.
Get a QuoteNVIDIA GPU Platforms
GPU server configurations based on NVIDIA platforms, including H100, H200 and A100, subject to availability.
Flexible GPU Configurations
Single- and multi-GPU servers configured around your workload, performance and scalability requirements.
High-Performance Infrastructure
GPU compute combined with high-speed networking and storage to support data-intensive AI and HPC workloads.
Deployment & Support
Our infrastructure team helps with configuration, deployment and ongoing support as your AI workloads evolve.
GPU Options
Choose the Configuration for Your Workload
Start with workload requirements, then confirm the hardware configuration and availability with our team.
GPU memory
Share model size, precision, context length and batch size so GPU memory requirements can be assessed for training or inference.
Single- or multi-GPU
Plan GPU count alongside workload parallelism, communication between GPUs and expected growth. Final sizing depends on your application.
AI Workloads & Use Cases
From Training to Production Inference
Training & Fine-Tuning
Size compute and GPU memory around your model, datasets, batch size and training approach.
AI Inference
Plan for response time, throughput and concurrent requests when serving trained models.
LLMs & Generative AI
Account for model size, context length and input or output data when planning text and generative workloads.
Computer Vision
Match GPU compute and data delivery to image resolution, video streams and processing volume.
High-Performance Computing
Review application compatibility, numerical precision and data movement for GPU-accelerated computing.
Beyond the GPUs
Networking, Storage & Host Infrastructure
Networking
Assess traffic between servers, storage and GPUs alongside bandwidth and latency requirements. Network design should reflect how the workload is distributed.
Learn More →Storage & Data Access
Plan capacity and read/write performance for datasets, model weights and checkpoints. Include data protection and retention requirements in the brief.
Learn More →Host Resources
Balance CPU, system memory and GPU resources around preprocessing, data loading and application needs.
Learn More →Deployment Process
From Requirements to a Dedicated GPU Environment
01
Define the Workload
Share your models, datasets, performance targets, software requirements and growth plans.
02
Agree the Configuration
Review GPU, memory, CPU, storage and networking choices, subject to hardware availability.
03
Deploy the Environment
Agree the deployment scope, responsibilities and validation criteria before the environment is handed over.
04
Plan Ongoing Support
Choose the management and support scope your team needs as workloads evolve.
Frequently Asked Questions
Planning Your AI Infrastructure
Which NVIDIA GPUs can I request?
Discuss H100, H200 and A100 configurations with our team. Hardware availability and the final configuration must be confirmed in your proposal.
Can the environment be configured for inference?
Yes. Share the model, expected request volume, context length and response-time targets so the configuration can be planned around your inference workload.
How many GPUs and how much memory do I need?
There is no single configuration for every model. Sizing depends on model size, precision, batch size, training or inference, and how the application distributes work.
Are drivers and AI frameworks included?
Software installation is not assumed to be included. Specify your operating system, drivers, frameworks and containers so the deployment responsibilities and scope can be agreed.
What should I include in a quote request?
Include your use case, model and dataset sizes, preferred GPU platform, memory needs, storage, connectivity, support requirements and target timeline.
Ready for what’s next?
Discuss Your AI & GPU Requirements
Share your workload and infrastructure requirements so we can prepare a configuration proposal.