Powerhouse in the Cloud: E2E Cloud Brings You Virtual GPU A100 40GB for Unmatched Speed and Flexibility!

Data scientists often face challenges working with large-scale datasets and complex machine-learning models. E2E Cloud understands these hurdles and is proud to offer the Virtual GPU A100 40GB, a groundbreaking solution designed to streamline data-intensive workloads while being cost-effective.

With Virtual GPU A100 40GB, data scientists can tackle their smaller workloads with unparalleled speed and efficiency. This virtual GPU leverages the advanced architecture of the NVIDIA A100 40GB GPU, renowned for its superior performance and cutting-edge technology. By harnessing the power of this GPU in the cloud, data scientists can accelerate their workflows and achieve faster insights, leading to improved productivity and decision-making.

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


NVIDIA Virtual GPU A100-40 GB GPU Card

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

Smaller Workloads, Greater Impact: Introducing E2E Cloud's Virtual GPU featuring A100 40GB

Here are some key features that make the Virtual GPU A100 40GB stand out:

Enhanced Performance and Speed

Data scientists can experience unmatched performance and accelerated computations with the virtual GPU of A100 40GB. It leverages the cutting-edge architecture of the A100 GPU, enabling faster data processing, model training, and iterative experimentation.

Seamless Scalability & Flexibility

E2E Cloud's virtual GPU allows data scientists to scale their GPU resources based on their requirements. They can easily adjust the number of virtual GPUs to accommodate varying workloads, ensuring optimal resource allocation and cost efficiency.

Cost-Effective Solution

By utilizing the virtual GPU of A100 40GB on E2E Cloud, data scientists can enjoy cost savings compared to dedicated physical GPUs. They can leverage the power of high-performance GPUs for their work without the need for investing in expensive hardware, resulting in reduced infrastructure costs.

Efficient Resource Utilization

With virtual GPU technology, multiple data scientists can share the same physical GPU, ensuring efficient resource utilization. E2E Cloud's virtual GPU platform intelligently manages resource allocation, allowing data scientists to collaborate seamlessly and optimize their productivity.

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

GPU Memory
Disk Space
Dedicated Ram
Hourly Billing
Weekly Billing
Monthly Billing
(Save 20%)
4 vCPUs
250 GB
30 GB
16 vCPUs
250 GB
115 GB

Why Choose vGPU A100 40GB?

1. GPU Virtualization:

The vGPU technology allows multiple data scientists to share the same physical GPU while maintaining isolation and providing a dedicated virtual GPU instance for each user. This enables collaboration, resource sharing, and cost-efficiency by maximizing GPU utilization.

2. Industry-Vetted Performance:

The A100 40GB GPU is recognized and widely adopted within the data science and machine learning communities. Its superior performance has been validated in numerous benchmarks and real-world applications, giving data scientists the confidence that they are working with in demand GPU technology.

3. Enhanced Performance:

The A100 40GB GPU, powered by NVIDIA's Ampere architecture, delivers exceptional performance for deep learning workloads. It features 6912 CUDA cores, 432 Tensor cores, and 40GB of high-bandwidth memory (HBM2), enabling accelerated training and inference for complex models.

4. Cost Optimization:

Data scientists often deal with smaller datasets, which may not require the full resources of a dedicated physical GPU. By leveraging a vGPU from E2E Cloud on the A100 40GB, data scientists can allocate the precise amount of GPU power they need, resulting in cost savings compared to purchasing and maintaining dedicated hardware.

Empowering Data Scientists: Top Real-World Use Cases of A100 40GB vGPU and Why E2E Cloud is the Right Choice

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

Model Training with Smaller Datasets

Data scientists often encounter scenarios where they need to train machine learning models on smaller datasets due to limited data availability. A vGPU of A100 40GB allows them to overcome memory constraints and efficiently train complex models with larger architectures, unlocking the potential for improved accuracy and performance. E2E Cloud's cost-effective pricing ensures that data scientists can train their models without breaking their budget.

Deep Learning and Neural Network Training

Deep learning models, particularly those based on convolutional neural networks (CNNs) or recurrent neural networks (RNNs), require immense computational power for training. A vGPU of A100 40GB offers the necessary horsepower to accelerate the training process, reducing the time required to achieve optimal model performance. E2E Cloud's reputation for industry-vetted performance makes it an excellent choice for data scientists who demand reliability and efficiency.

Natural Language Processing (NLP) Applications

NLP tasks, such as language translation, sentiment analysis, or text generation, often involve working with large language models. These models demand significant computational resources during both training and inference stages. A vGPU of A100 40GB can handle the heavy workloads of NLP tasks, enabling data scientists to develop state-of-the-art language models with faster iteration cycles. E2E Cloud's competitive pricing ensures that data scientists can scale their NLP applications without financial constraints.

Computer Vision and Image Processing

Computer vision applications, including image recognition, object detection, and semantic segmentation, require substantial computational capabilities. The A100 40GB vGPU's high-performance CUDA cores and tensor cores excel at accelerating image-related tasks, allowing data scientists to train complex vision models more efficiently. E2E Cloud's reliable infrastructure ensures data scientists can process and analyze large volumes of visual data effectively.

Generative Adversarial Networks (GANs)

GANs have revolutionized various domains, such as image synthesis, data augmentation, and video generation. Training GANs demands significant computational resources due to the intricate interplay between generator and discriminator networks. A vGPU of A100 40GB delivers the necessary power to train GANs effectively, enabling data scientists to create realistic and high-quality synthetic data. E2E Cloud's cost-effective pricing makes it an attractive choice for data scientists experimenting with GANs.

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.

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