GPU Computing – Series G 

Leverage E2E Networks GPU instances for compute and graphics-intensive workload. Our GPU’s offer high performance and cost-effective means for compute and graphics-intensive processing power, helping you to fuel innovation. It is ideal for workloads such as machine learning, artificial intelligence, graphics rendering, video editing, remote visualization, high-performance computing and many other parallel workloads.

E2E Networks GPU instances are based on NVIDIA’s GeForce® GTX 1080 Ti-series graphics cards which are powered by Pascal™ microarchitecture to deliver breakthrough performance. It can act as a co-processor or can be used for graphics acceleration, thanks to its CUDA cores.

GeForce GTX 1080 Ti:

  • 3584 CUDA cores
  • 484 GB/s maximum bandwidth
  • Nvidia GPU Boost 3.0 Technology


A high-performance parallel Computing Solution

Transparent Pricing Model

Low-cost GPU instances with transparent pricing model & no hidden costs.


High-Performance Compute with the latest gen. CPU + RAM + GPU + SSD.

Increase ROI

No upfront cost and up to 60% cost savings as compared to competitors

Reduce Processing Time

Ideal for parallel processing workloads to process large block of data at once.

Beneficial for Workloads

  • Graphic Design – Ideal for graphics designing, engineering applications, architects, non-linear editing, distance learning and 3D graphics.
  • Video RenderingConduct video transcoding from HD to Ultra-HD, live broadcasting, video conferencing, video signal processing, source film repair, etc.
  • ComputingExcellent for 3D rendering, animations, digital image processing, financial computing applications, and scientific computing.
  • Deep Learning – Highly suitable for image recognition applications, video content identification, business intelligence, machine learning and other applications.

Why GPU?

A GPU (Graphical Process Unit), has a parallel compute architecture in comparison to a CPU. The initial GPU usage was for computer graphics (OpenGL/Direct3D) but with CUDA, a parallel computing platform and API allows developers to directly access GPU’s virtual instruction set and parallel computational elements. The applications such as big data analytics, machine learning, deep learning, Artificial Intelligence can take the benefit of parallelization and can perform the operations much faster than a CPU.

A GPU consists of thousands of stream processors which are slower in comparison to a CPU core but it munches the data in parallel workloads which increases the overall performance.

Check our plans to get started

₹ 17,249** Per Month or
₹ 23.63* Per Hour
OS: CentOS / Ubuntu
Dedicated RAM: 30 GB
Disk Space: 450 GB SSD
GPU: 1 X NVIDIA GP102 [GeForce GTX 1080 Ti]
₹ 34,499** Per Month or
₹ 47.26* Per Hour
12 VCPUs
OS: CentOS / Ubuntu
Dedicated RAM: 62 GB
Disk Space: 900 GB SSD
GPU: 2 X NVIDIA GP102 [GeForce GTX 1080 Ti]

*  Price is exclusive of 18% GST Rate
**Monthly Prices shown are calculated using an assumed usage of 730 hours per month; actual monthly costs may vary based on a number of days in a month.

To get started check out our help article on E2E Networks GPU Instances