Entry Level Cloud GPUs

Entry-level cloud GPUs are carved out of A100-80 GB NVIDIA GPU cards using MIG technology.

With MIG (Multi-Instance GPU), customers will be able to see and schedule jobs on their virtual GPU instances as if they were physical GPUs. MIG works with Linux operating systems, supports containers using Docker Engine, with support for Kubernetes. MIG allows multiple vGPUs (and thereby VMs) to run in parallel on a single GPU while preserving the isolation guarantees that vGPU provides. These entry level Cloud GPUs are best suited for students and data scientists with minimum data sets.

Learn more about NVIDIA A100-80GB
NVIDIA A100-80GB Data SheetNVIDIA MIG User Guide

Specs

NVIDIA A100-80 GB GPU Card

6912
CUDA Cores(Parallel-Processing)
432
Tensor Cores (Machine & Deep Learning)
80 GB HBM2
GPU Memory
2039 GB/s
GPU Memory Bandwidth
NVLINK
Form Factor
Peak FP64
9.7 TFLOPS

Benefits of E2E Cloud GPUs

No Hidden Fees

No hidden or additional charges. What you see on pricing charts is what you pay.

NVIDIA Certified Elite CSP Partner

We are NVIDIA Certified Elite Cloud Service provider partner. Build or launch pre-installed software Cloud GPUs to ease your work

NVIDIA Certified Hardware

We are using NVIDIA certified hardware for GPU accelerated workloads.

Flexible Pricing

We are offering pay as you go model to long tenure plans. Easy upgrade allowed. Option to increase storage allowed.

GPU-accelerated 1-click NGC Containers

E2E Cloud GPUs have super simple one click support for NGC containers for deploying NVIDIA certified solutions for AI/ML/NLP/Computer Vision and Data Science workloads.

Linux A100 -80 GB - Entry Level Cloud GPUs

Plan
GPU Memory
vCPUs
Disk Space
Dedicated Ram
Hourly Billing
Weekly Billing
Monthly Billing
(Save 20%)
VGC3.A10010-4.30GB
10
4 vCPUs
250 GB
30GB
₹30/hr
₹4,950/week
₹15,000/mo

Multiple Use-cases, One Solution!

E2E’s GPU Cloud is suitable for a wide range of uses.

AI Model Training and Inference:

Earlier, GPUs were confined to perform domain-specific tasks with either training or inference. With NVIDIA A100, you can get the best of both worlds with an accelerator for training as well as inference. Compare to earlier cards, we can speedup training and inference by 3X to 7X.

Image/Video Decoding:

One of the significant challenge in achieving high end-to-end throughput in a DL platform is to be able to keep the input video decoding performance matching the training and inference performance. A100 GPU fixed this by adding 5 NVDEC (NVIDIA DECode) unit compared to 1 Unit in earlier GPU Card.

High-Performance Computing:

A100 introduces double-precision Tensor Cores, this enables researchers to reduce a 10-hour, double-precision simulation running on NVIDIA V100 Tensor Core GPUs to just four hours on A100. HPC applications can also leverage TF32 precision in A100’s Tensor Cores to achieve up to 10X higher throughput for single-precision dense matrix multiply operations.

Language Model Training:

Natural Language Processing (NLP) has seen rapid progress in recent years. It is no longer possible to fit large models parameters in the main memory of even the largest GPU. NVIDIA A100 is the flagship product of the NVIDIA which is the only solution can run 1-trillion-parameter model in reasonable time with the scaling feature of A100-based systems connected by the new NVIDIA NVSwitch and Mellanox State of Art InfiniBand and ethernet solution.

Deep Video Analytics:

Media publishers to surveillance systems, deep video analytics is the new vogue for extracting actionable insights from streaming video clips. NVIDIA A100’s memory bandwidth of 1.5 terabytes per second makes it perfect choice for image recognition, contactless attendance, and other deep learning applications.

Accelerate Machine Learning and Deep Learning Workloads with up to 70% cost-savings.

Benefits of using E2E Cloud GPUs for startups

Check this review by Mr. Prashant Kumar, CEO of Eigenlytics to know the benefits they got using E2E Cloud GPUs for their model training requirements.

How E2E GPU Cloud is helping Cloud Quest in their gaming journey

Latency is a critical part of Cloud Gaming. E2E GPU Cloud provided ultra-low network latency to Cloud Quest users and enhanced their gaming experience.