Data Center GPUs

Data Center GPUs

High-Performance Data Center GPUs for Modern Workloads

Power next-generation AI, HPC, and enterprise applications with Cyfuture Cloud’s advanced Data Center GPUs. Achieve faster training, low-latency inference, and superior parallel processing on secure, scalable infrastructure engineered for always-on performance.

Cut Hosting Costs!
Submit Query Today!

Data Center GPUs Explained

Data Center GPUs represent specialized graphics processing units engineered for enterprise-scale computing, distinct from consumer graphics cards through their focus on parallel processing, high memory bandwidth, and 24/7 operational reliability. NVIDIA's A100, H100, and H200 series exemplify Data Center GPUs, featuring massive HBM memory capacities up to 141 GB, NVLink interconnects for multi-GPU scaling, and optimized architectures for AI training, scientific simulations, and data analytics. These GPUs deliver exceptional FP64 precision for HPC workloads while supporting FP8 and BF16 formats for accelerated AI inference, making them essential for cloud providers, research institutions, and enterprises processing petabyte-scale datasets. Deployed in rack-scale servers with liquid cooling and redundant power systems, Data Center GPUs ensure sustained performance under maximum loads within secure, compliant data center environments.

What Are Data Center GPUs?

Data Center GPUs are specialized graphics processing units engineered for high-performance computing environments, optimized for parallel processing tasks like AI training, machine learning inference, scientific simulations, and big data analytics. Unlike consumer GPUs focused on graphics rendering, Data Center GPUs feature enterprise-grade reliability, scalability, and advanced memory architectures such as HBM3 to handle massive datasets efficiently. These GPUs power modern hyperscale data centers, enabling organizations to accelerate complex workloads that traditional CPUs cannot process at comparable speeds.

How Data Center GPUs Work

Massive Parallel Processing

Thousands of smaller cores execute simple operations simultaneously, ideal for matrix multiplications and vector calculations common in AI and HPC workloads.

High-Bandwidth Memory (HBM)

Utilizes stacked HBM memory providing terabytes-per-second bandwidth for rapid data access during intensive training and inference tasks.

Tensor Cores Acceleration

Dedicated tensor cores perform mixed-precision matrix math optimized for deep learning frameworks like TensorFlow and PyTorch.

NVLink Interconnects

High-speed GPU-to-GPU communication enables multi-GPU scaling with up to 900 GB/s bandwidth for distributed training across server clusters.

Multi-Instance GPU (MIG)

Partitions a single GPU into isolated instances for secure and efficient resource sharing across multiple tenants or workloads.

Transformer Engine

Provides hardware acceleration for transformer models with dynamic precision scaling between FP8 and FP16 for optimal performance.

NVSwitch Fabric

Scalable switching technology that connects hundreds of GPUs in massive clusters, enabling exascale computing applications.

Technical Specifications - Data Center GPUs

GPU Models & Architecture

  • Supported GPU Series: NVIDIA A100, NVIDIA A40, NVIDIA H100 (model availability may vary)
  • GPU Architecture: NVIDIA Ampere / NVIDIA Hopper
  • GPU Type: Data Center / AI Compute Accelerators
  • GPU Memory: 40 GB – 80 GB HBM2 / HBM3 (varies by model)
  • Memory Bandwidth: Up to 2,000 GB/s+
  • FP32 Performance: Up to 19.5 TFLOPS (A100) / Higher on H100
  • FP16 Performance: Up to 312 TFLOPS (Tensor Cores)
  • Tensor Performance: Up to 1,250 TFLOPS (AI Mixed-Precision)

Compute & AI Acceleration

  • CUDA Cores: Up to 6,912+ (varies by model)
  • Tensor Cores: 3rd / 4th Generation (AI acceleration)
  • NVLink Bandwidth: Up to 600 GB/s (GPU-to-GPU scaling)
  • Multi-GPU Scaling: Support for NVLink & DGX-style scaling
  • PCIe Interface: PCIe Gen4 x16
  • GPU Count per Node: 1, 2, 4, 8 (Configurable)

Memory & Storage

  • GPU Memory Type: HBM2 / HBM3
  • GPU Memory Size: 40 GB / 80 GB
  • Local NVMe Storage: Up to 2 TB (per node)
  • Storage Type: NVMe SSD (Enterprise-grade)
  • Cached Dataset Support: Local-cached datasets for large-scale AI training

Networking

  • Data Center Networking: 100 GbE / 200 GbE / 400 GbE
  • RDMA Support: RoCE v2 (low-latency distributed training)
  • GPU-Cluster Fabric: High-speed interconnects for distributed AI jobs

Software & Ecosystem

  • Supported OS: Linux (Ubuntu / CentOS / Rocky)
  • Drivers & Runtime: NVIDIA CUDA Toolkit, cuDNN, NCCL
  • AI Frameworks: TensorFlow, PyTorch, MXNet, JAX, ONNX
  • Container Support: Docker / NVIDIA Container Toolkit
  • Orchestration: Kubernetes + GPU scheduling
  • Security: Secure Boot, TPM 2.0, Isolated Tenant Environments

Management & Monitoring

  • Remote Management: IPMI / Redfish
  • GPU Monitoring: NVIDIA-SMI, DCGM, Prometheus Integration
  • Auto-Scaling Support: Horizontal GPU workload scaling
  • Usage Billing: Per-minute / per-second GPU billing options

Power & Cooling

  • PSU Efficiency: 80+ Platinum / Titanium
  • Redundant Power: N+1 Redundancy
  • Cooling: Liquid-cooled / High-efficiency Air-cooled (depending on rack)
  • Rack Density: High-density GPU racks supported

Security & Compliance

  • Data Encryption: AES-256 at rest & in transit
  • Network Isolation: VLAN / VPC support
  • Identity & Access: RBAC (Role-Based Access Control)
  • Compliance: ISO 27001, SOC 2 (where applicable)

Key Highlights of Data Center GPUs

Parallel Processing Power

Data Center GPUs excel at handling thousands of simultaneous computations, accelerating AI training and complex simulations by orders of magnitude compared to traditional CPUs.

AI Workload Acceleration

Specialized Tensor Cores optimize deep learning operations, enabling faster model training, inference, and large language model processing for enterprise AI deployments.

Energy Efficiency Gains

Deliver higher FLOPS per watt than CPUs for parallel workloads, significantly reducing power consumption and operational costs in large-scale data center environments.

Scalable Multi-GPU Support

NVLink and high-speed interconnects enable seamless scaling across multiple GPUs, supporting massive datasets and distributed computing for hyperscale applications.

High-Performance Computing

Purpose-built for scientific simulations, weather modeling, and financial analytics, processing petabytes of data with precision FP64/FP32 compute capabilities.

Real-Time Data Analytics

Process streaming data and big data analytics up to 100x faster, enabling real-time insights for business intelligence and fraud detection systems.

Cost-Effective Scalability

Pay-as-you-go cloud models and efficient resource utilization minimize upfront hardware costs while maximizing throughput for variable workloads.

Enterprise Reliability

ECC memory, redundant cooling, and 24/7 professional support ensure mission-critical stability for production AI and HPC environments.

Why Choose Cyfuture Cloud for Data Center GPUs

Cyfuture Cloud stands out as the premier choice for Data Center GPUs, offering access to enterprise-grade NVIDIA GPUs like H100, H200, and RTX A5000 in MeitY-empanelled data centers across India. With seamless Kubernetes-native deployment, users benefit from high-performance computing optimized for AI training, inference, and HPC workloads. The platform ensures data sovereignty, low-latency connectivity through multiple Tier-1 ISPs, and robust security features including encryption and compliance with global standards. Whether scaling large language models or processing massive datasets, Cyfuture Cloud delivers the computational power needed for mission-critical applications without the overhead of on-premises infrastructure.

What truly differentiates Cyfuture Cloud for Data Center GPUs is its cost-effective, pay-as-you-go pricing combined with 99.99% uptime SLAs and 24/7 expert support. Multi-GPU configurations with NVLink interconnect enable unprecedented scalability, while automated provisioning accelerates time-to-insight for data scientists and developers. From startups to enterprises, Cyfuture Cloud eliminates hardware procurement delays and maintenance burdens, providing flexible GPU rental options tailored to diverse workloads. Choose Cyfuture Cloud to harness Data Center GPUs that drive innovation, reduce TCO, and ensure future-ready performance in the cloud.

Certifications

  • SAP

    SAP Certified

  • MEITY

    MEITY Empanelled

  • HIPPA

    HIPPA Compliant

  • PCI DSS

    PCI DSS Compliant

  • CMMI Level

    CMMI Level V

  • NSIC-CRISIl

    NSIC-CRISIl SE 2B

  • ISO

    ISO 20000-1:2011

  • Cyber Essential Plus

    Cyber Essential Plus Certified

  • BS EN

    BS EN 15713:2009

  • BS ISO

    BS ISO 15489-1:2016

Awards

Testimonials

Technology Partnership

  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership
  • Technology Partnership

FAQs: Data Center GPUs

#

If your site is currently hosted somewhere else and you need a better plan, you may always move it to our cloud. Try it and see!

Grow With Us

Let’s talk about the future, and make it happen!