Save Big: Up To 10% Off On Multiple GPU Servers!

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Features of the NVIDIA A100 GPU Hosting

Hosted GPU dedicated servers with NVIDIA A100 GPUs carry out excellent performance over integrated graphics. Unmatched performance for AI model training, data inference, deep learning, and complex simulations, guaranteeing high scalability and productivity in enterprise-level hosting cases.

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MULTI-INSTANCE GPU (MIG)

An A100 GPU can be easily divided into 7 different GPUs, completely isolated at the level of hardware with their individual computing cores, cache memory, and high-bandwidth memory. MIG gives all developers full access to boost acceleration for various applications, and IT administrators can provide appropriate GPU acceleration for every work.

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NVIDIA AMPERE ARCHITECTURE

Whether utilizing MIG to divide an A100 GPU into small portions or NVLink to link various GPUs to speed heavy workloads, A100 can easily manage various-sized acceleration requirements, from the little job to the large-scale heavy workload.The versatility of the NVIDIA A100 states that all IT managers can easily increase the utilization of GPU servers in their data centers.

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STRUCTURAL SPARSITY

AI networks have a huge amount of useful parameters. Not every single parameter is required for correct estimations, and some can be easily changed to zeros, making all the models “sparse” without negotiating with accuracy. While the feature of sparsity more easily advantages AI inference, it can also enhance the working of AI model training.

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NEXT-GENERATION NVLINK

NVIDIA NVLink in the case of A100 offers two times more accurate output as compared to the former generation. When united with NVIDIA NVSwitch™, up to almost 16 A100 GPUs can be easily interlinked at up to 600 gigabytes every second (GB/sec), uniting the maximum application performance that is possible on a solo server.

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THIRD-GENERATION TENSOR CORES

NVIDIA A100 has 312 teraFLOPS (TFLOPS) of performance related to deep learning. That is almost 20 times the Tensor floating-point operations per second (FLOPS) especially for deep learning model training and also 20X the Tensor tera operations per second (TOPS) for deep learning inference as compared to Volta GPUs of NVIDIA.

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HIGH-BANDWIDTH MEMORY (HBM2E)

With up to almost 80 gigabytes of HBM2e, the A100 GPUs have the most powerful GPU memory bandwidth of almost more than 2 TB per second and a dynamic random-access memory (DRAM) usage productivity of 95 percent. A100 offers 1.7 times more memory bandwidth as compared to the former generation.

Flexible Plans to Maximize Your NVIDIA A100 Server Performance

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Enterprise GPU - A100

$ 869.00/month

$50.99
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
  • Fully managed
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Save 40%
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Multi-GPU - 4xA100

$ 2,569.00/month

$50.99
  • Dual 22-Core E5-2699v4
  • 512 GB RAM
  • 4TB NVMe
  • 1Gbps Port Speed
  • GPU: 4 x Nvidia A100
  • Microarchitecture: Ampere
  • Max GPUs: 4
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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  • Instant Support
  • Quick Deploy
  • Robust Security

When to select a GPU Server A100 Hosting

The platform boosts over 2,000 apps, containing every single deep learning agenda. A100 is almost available at every place,
from desktops to servers to cloud services, carrying out both optimal performance gains and money-saving options.

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Deep Learning Inference

A100 have advanced-level features to enhance heavy workloads. It boosts a wide variety of precision, ranging from FP32 to INT4. Multi-Instance GPU (MIG) allows various networks to operate at the same time on a single A100 for reliable usage of computing resources. And structural sparsity always supports offering almost two times more reliable performance on top of A100’s some other inference performance advantages.

The A100 is developed to boost AI model utilization and inference tasks, allowing quicker and more productive processing of rigid neural networks. Its innovative capabilities support reliable and accurate estimations, making it one of the best choices for all demanding AI applications in the case of data centers and research landscapes.

High-Performance Computing

NVIDIA A100 presents double-precision tensor cores to carry out the robust leap in HPC performance since GPUs are introduced. With 80GB of the quickest GPU memory, researchers can easily decrease up to 10-hour, double-precision complex simulation to under almost 4 hours on the A100 server. HPC applications can easily influence TF32 to attain up to 11x more accurate outcomes for single-precision, solid matrix-multiply processes.

Its Ampere architecture improves parallel processing and productivity, making it one of the best choices for handling scientific simulations, data analysis, and complex computations. The A100’s innovative features allow quicker implementation of demanding heavy workloads, determining progressions in engineering, scientific research, and data-accelerated applications.

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Deep Learning Training

AI training models are exploding in the case of complexity as they easily take on high-level challenges like conversational artificial intelligence. Training all of them needs intensive computing power and reliability. NVIDIA A100 Tensor Cores with Tensor Float (TF32) offers up to 20X reliable performance over the NVIDIA Volta with almost no changes in the code and double acceleration with automatic integrated precision and FP16.

Its innovative Ampere architecture boosts the training of extensive neural networks, fundamentally decreasing the time needed to process and enhance all complex models. With improved Tensor Cores and MIG capabilities, the A100 helps in productive and reliable training for cutting-edge AI applications and research.

High-Performance Data Analytics

Data scientists need to be easily able to examine, visualize, and turn all large datasets into useful insights. But expanded outcomes are frequently hung up by huge datasets dispersed across various servers. Boosted servers with A100 offer the required computing power—including extra memory, over almost 2 TB per sec of memory bandwidth, and reliability with NVIDIA® NVSwitchTM and NVLink® to manage all heavy workloads.

Its Ampere architecture boosts data processing and valuations, allowing quick insights from datasets. The progressive features of A100, containing tensor cores and high memory bandwidth, enable quicker querying and real-time data analytics, driving outcomes in various fields like healthcare, finance, engineering, and scientific research.

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Substitues of GPU Dedicated Servers with NVIDIA V100

If you need to perform research, deep learning training, and many more, then you can have some better options.

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NVIDIA V100

Offers robust performance along with 16 GB of HBM2 memory and 640 tensor cores, best for AI model training and many more.


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Tesla k40

The Tesla K40 offers robust computing power with 12 GB of GDDR5 memory and 2880 CUDA cores, ideal for complex computing.


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Frequently Asked Questions

The Nvidia A100 usually supports heavy workloads like AI model, HPC-related tasks, and complex simulations because of its high processing power and more capacity of memory.

This type of servers are specially designed for carrying out all deep learning related tasks by simply providing excellent performance with its integrated Ampere architecture.

While the A100 is specially designed for carrying out advanced computing, deep learning, and data analytics instead of playing games. Moreover, it is not specifically developed for playing games, and user-oriented GPU servers such as the GeForce series are an ideal option for gaming.

The A100 has almost 6912 CUDA cores, which always benefits in its providing unmatched computing power for carrying out all complex tasks and difficult calculations.

The Nvidia A100 always surpasses the NVIDIA V100 simply by having the maximum number of CUDA cores, more memory, and increased productivity just because of its Ampere architecture.

Several crucial components of this GPU consist of 40 GB of HBM2 memory, 6912 CUDA cores, tensor cores for AI model training, and complete support for MIG technology.

Yes, this category of card integrated GPU server is mainly utilized for the purpose of performing AI model training, enhancing every single procedure with its large memory size and robust computing capabilities.

The accelerated processing power and also optimal storage capacity of the A100 server allow rapid data processing and actual ray-tracing, making it one of the best options for managing all data analytics tasks at an advanced-level.