Maximum performance on dedicated GPUs

Bare-metal GPU servers give your models the dedicated resources they need for the heaviest computation. With no virtualization layer and no shared tenancy, every cycle goes to your workload, so training runs finish faster and inference stays responsive under load.

Each system pairs high-core-count AMD EPYC processors with large memory and fast NVMe storage, so data moves quickly from disk to GPU and large models stay resident in memory.

• Dedicated GPUs with no noisy-neighbor effect
• High-core-count AMD EPYC Genoa processors
• 192 GB to 2.25 TB of RAM per server
• Fast NVMe SSD storage for large datasets
• Faster training and low-latency inference
• Single and dual-socket configurations

Scale as your AI program grows

Add capacity as your projects evolve. Provision additional GPU servers, combine dedicated hardware with public cloud for hybrid workloads, and align resources to demand without re-architecting your platform.

Pricing stays transparent and predictable, so you capture the long-term value of dedicated hardware and keep budgets under control, free of the variable costs that come with shared or virtualized environments.

Security, control, and data residency

Physically isolated, single-tenant servers give you a hardened foundation for sensitive AI work. With full root access you control the operating system, the frameworks, and every security measure applied to your environment.

Deploy close to your data and users across a global network of data centers, many positioned near major exchanges and trading venues, and keep complete control over data residency and compliance.

• Physically isolated, single-tenant bare metal
• Full root access and complete stack control
• Global data centers near major venues
• Data residency and compliance control
• Hardening tailored to your applications
• Sustainable, water-cooled infrastructure

Built for every AI workload

From research to production, the same infrastructure handles the full range of modern AI. Train and iterate on deep-learning models with speed and precision, run natural-language and conversational AI, deliver high-fidelity computer-vision tasks from image recognition to autonomous navigation, and run high-throughput data analysis.

The massive parallel processing of dedicated GPUs also makes these servers well suited to building and running large language models, where memory capacity and throughput are decisive.

Why teams build on Modulus

Beyond raw performance, Modulus brings deep expertise in high-performance computing and AI. Our teams understand the demands of machine learning and deep learning, and help you optimize deployments and resolve complex issues quickly.

Your stack stays open. The platform integrates cleanly with leading frameworks including TensorFlow, PyTorch, and Keras, so your engineers build with the tools they already know, free of vendor lock-in. A geographically diverse network keeps uptime high and latency low, wherever your data and users sit.