Unlock AI Performance with our High-Performance GPU Servers
We offer enterprise-grade GPU (Graphics Processing Unit) servers purpose built for deep learning, generative AI and real-time inference.
- Vertical Data’s AI solutions are driven by cutting-edge chips from Tier-1 manufacturers, including NVIDIA and AMD’s enterprise-grade portfolio, spanning high-performance to mid-range offerings.
We ship hardware in weeks (not months) and have several flexible financing solutions depending on your needs.
Key Features of Our GPU Servers: Powering Your AI Infrastructure
Industry-Leading GPU Performance
Configurable with cutting-edge NVIDIA and bleeding edge GPUs. Engineered for deep learning, generative AI, and HPC with optimal efficiency.
Advanced Interconnectivity
NVLink and InfiniBand enable high-speed GPU-to-GPU communication for efficient model parallelism.
High-Bandwidth Memory (HBM)
Ultra-fast HBM3 and PCIe Gen5 interconnects for maximum throughput.
Scalable Performance
Multi-GPU, multi-node support for distributed training and inference.
AI Framework Optimization
Pre-optimized for TensorFlow, PyTorch, JAX, ONNX, and Hugging Face Transformers.
Energy-Efficient Design
Air and liquid cooling options for high-density deployments with reduced power usage.
Our Clients Love Vertical Data
Vertical Data Inc. has provided unmatchable customer service with a tremendous quality of service that has resulted in fast deliverables. We have peace of mind knowing there is a team of experts behind us, guaranteeing high levels of service and solutions that give our business a competitive edge.
Our dealings with Vertical Data Inc. have been seamless, I can't say enough how much I appreciate the fast turnaround time, aggressive cost structure and on-time delivery.
The Vertical Data Solution
Performance & Scalability: Designed for AI Optimization
AI Model Training at Scale
Server clusters built to handle large-scale deep learning models, generative AI, and LLMs.
Low-Latency Inference
Optimized for real-time applications, including chatbots, recommendation engines, vision optimization and edge AI.
Elastic Scaling
Deploy single nodes or multi-cluster GPU arrays, adapting to dynamic AI workloads.
On-Prem, Cloud, or Hybrid
Flexible deployment options to fit enterprise data center, colocation, or cloud environments.
Vertical Data Delivers
Turnkey AI Deployments
Plug-and-Play AI Deployment:
Servers pre-configured with optimized drivers (CUDA, ROCm) and AI toolkits. Seamless hybrid/multi-cloud integration with AWS, Azure, Google Cloud, and private clouds.
Hybrid & Multi-Cloud Interconnects:
Seamless integration across hybrid, private and multi-cloud environments (AWS, Azure, Google Cloud).
Network of High Density Colocation:
We have a global network of high density colocation facilities to deploy your AI hardware - complete with managed services.
Enterprise & Open-Source AI Support:
Compatible with Kubernetes, Ray, MLflow, and leading AI frameworks for flexible orchestration.
Vertical Data Delivers Turnkey AI Deployments
End-to-end AI infrastructure provider offering hardware provisioning, data center integration and managed services for large or small AI deployments. Below are key features and benefits that set our turnkey AI solutions apart from the rest:
Hardware Provisioning
Delivers purpose-built appliances tailored to specific AI use cases, optimizing performance and efficiency.
Data Center Integration
Operates a worldwide network of high-density data centers, optimized for enterprise AI scalability and performance.
Managed Services
Supports multi-cloud implementations with adaptable pricing models, enhancing flexibility for enterprise AI solutions.
Use Cases & Industries
Autonomous Vehicles & Robotics
Computer vision sensors & predictive maintenance automation.
Financial Services & AI Trading
Accelerated risk modeling, fraud detection, and algorithmic trading.
Healthcare & Drug Discovery
Medical imaging analysis Genomics research Real-time monitoring.
Financial Services & AI Trading
Accelerated risk modeling, fraud detection, and algorithmic trading.
Media & Entertainment
Content generation, video rendering, and deepfake detection.
Retail & E-Commerce
Demand forecasting, personalized marketing, supply chain automation.
Start Your Journey Today
Assess Requirements
Work with our expert technical team to determine your needs – and how we can help.
Customized Financing Plans
We work with your technical team to spec out a full deployment – including pricing, financing terms and delivery times.
Procure & Deploy
We work with our deep distribution network to order, ship and deploy your hardware solutions.
Continuously Optimize
We provide continued support – from extended hardware warranties to maintenance and follow-on support.
Enterprise GPU Servers FAQs
What is the difference between a GPU and a CPU?
A GPU is designed for handling parallel processing tasks, making it ideal for complex computations like rendering graphics and performing matrix operations. CPUs (Central Processing Units), on the other hand, excel at executing general-purpose tasks sequentially, making them well-suited for traditional computing tasks.
Why are GPU servers important for machine learning?
GPU servers offer the computational power needed to process large datasets and train complex machine learning models efficiently. Their ability to handle parallel computations allows for quicker iteration times, making them essential for cutting-edge machine learning tasks.
Are GPU servers useful for artificial intelligence applications?
Yes, GPU servers play a critical role in artificial intelligence by accelerating the processing of neural networks, supporting deep learning frameworks, and handling the massive calculations required for AI-related tasks.
Do you provide support for hardware maintenance and optimization?
Yes, we offer continued support, including extended hardware warranties, regular maintenance, and follow-up services, to ensure your GPU server solutions remain operational and optimized over time.
How do GPU servers integrate with other processors in a deployment?
GPU servers can work alongside CPUs in a hybrid ecosystem, with CPUs handling general-purpose tasks while GPUs manage intensive processing workloads. This division maximizes performance and efficiency in technical deployments.