Build the Scalable Computational Power to Accelerate AI and ML Workloads Across Your Infrastructure

Modern cloud environments must remain flexible and rapidly evolving as per the technology stack to run AI and ML workloads, GPU instances, High-performance computing (HPC), and data analytics. Our GPU infrastructure is the source of such a massive transformation that spins up the maximum performance and scalability for large-scale data processing engines at unimaginable speeds. Embrace the game-changing infrastructure, powered by our highly skilled engineering teams.

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GPU Architecture

Combine the most powerful hardware, optimized drivers, and enterprise-grade software stacks – making it an intelligent architecture. Our teams design GPU-powered architectures that maximize throughput, minimize latency, and deliver production-grade AO and HPC performance on a scale.

Hardware

GPU physical components including CUDA and tensor cores, VRAM, and memory controllers. It determines the raw computing capabilities and GPU efficiency.

Firmware and Drivers

Intermediatory components between hardware and software, used for increasing performance, optimizing power consumption, and triggering compatibility while running various workloads.

Software and APIs

Includes programming interfaces such as CUDA, OpenCL, Vulcan, and DirectX for GPU acceleration and computational power. Whereas TensorFlow and PyTorch APIs are for AI workloads for enterprises to develop scalable ML models with real-time data processing.

Why Use GPU Instances on Hyperscalers?

Running the GPU infrastructure on major hyperscalers makes it free from virtualization overhead. When NVIDIA and AMD GPUs are embedded into every layer of your infrastructure, here are the aspects they deliver:  

  • 10X Faster Model Training: Reduce time-to-market for AI-ready solutions
  • Up to 3200 Gb/Sec Network Bandwidth
  • Scale to 131,072 GPUs for Large-Scale LLM Training
  • 99.99% Availability
  • 90% Lower Latency

Key Components of GPU Infrastructure

GPU infrastructure is built by using several key components, varying from enterprise to enterprise but here are the common components that makes data processing and AI workload efficient.  

Enable intensive computations using the physical hardware and suitable for high-performance computing scenarios. 

Access GPU power from any location with the help of workstations we build for you and run powerful applications on hyperscalers seamlessly.  

GPU infra on cloud and data centers enables centralized management and powers high-speed network capabilities.  

Handle AI/ML tasks and a huge volume of datasets efficiently with the parallel processing potential that GPU clusters offer for your AI applications.  

Run near infinite parallel computations and operate complex calculations at scale for faster insights to realize business goals more effectively. 

Our Key Differentiations

End-to-End Architecture Design – can be enabled across all the cloud environments

Workload Profiling and Rightsizing – right GPU recommendations & optimization of VM/bare metal deployments

High-Performance Network Expertise – RDMA over Converged Ethernet (RoCE) and Cluster-Aware Scheduling implementations

Automated Provisioning and Scaling – auto-scaling clusters and Infrastructure-as-Code (IaS)

Data Pipeline Optimization – high-throughput storage by design and dataset training

AI/MLOps Integration – deployment of MLOps pipelines for automated model training, versioning, and deployment

Our Customer Successes