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Dedicated A100 GPU Hosting, NVIDIA A100 Rental

The NVIDIA A100 Tensor Core GPU delivers unprecedented acceleration—at every scale—to power the world’s highest-performing elastic data centers for AI, data analytics, and HPC applications. A100 can efficiently scale up or be partitioned into seven isolated GPU instances with Multi-Instance GPU (MIG), providing a unified platform that enables elastic data centers to dynamically adjust to shifting workload demands.
Dedicated A100 GPU Hosting, Rent NVIDIA A100 GPU Server

NVIDIA A100 40 GB TENSOR CORE GPU SPECIFICATIONS

NVIDIA A100 Tensor Core technology supports a broad range of math precisions, providing a single accelerator for every workload.
Specifications
GPU Microarchitecture
Ampere
CUDA Cores
6,912
Tensor Cores
432
Memory
40GB HBM2e
Memory Clock Speed
1215 MHz
Memory Bus Width
5120 bit
Memory Bandwidth
1,555 GB/s
FP16 (float) performance
77.97 TFLOPS (4:1)
FP32 (float) performance
19.49 TFLOPS
FP64 (float) performance
9.746 TFLOPS (1:2)
FP64 Tensor Core
19.49 TFLOPS
FP64
9.7 TFLOPS
Boost Clock
1410 MHz
Base Clock
1095 MHz
Technology Support
Tensor Cores
3rd Generation
OpenGL
N/A
OpenCL
3.0
DirectX
N/A
CUDA
8.0
Shader Model
N/A
Other Specifications
TDP
400W
System Interface
PCIe 4.0 x16

Nvidia A100 GPU Hosting Server Features

Hosted dedicated servers with A100 graphics delivers superior performance over integrated graphics.
NVIDIA AMPERE ARCHITECTURE
NVIDIA AMPERE ARCHITECTURE
Whether using MIG to partition an A100 GPU into smaller instances or NVLink to connect multiple GPUs to speed large-scale workloads, A100 can readily handle different-sized acceleration needs, from the smallest job to the biggest multi-node workload. A100’s versatility means IT managers can maximize the utility of every GPU in their data center, around the clock.
THIRD-GENERATION TENSOR CORES
THIRD-GENERATION TENSOR CORES
NVIDIA A100 delivers 312 teraFLOPS (TFLOPS) of deep learning performance. That’s 20X the Tensor floating-point operations per second (FLOPS) for deep learning training and 20X the Tensor tera operations per second (TOPS) for deep learning inference compared to NVIDIA Volta GPUs.
NEXT-GENERATION NVLINK
NEXT-GENERATION NVLINK
NVIDIA NVLink in A100 delivers 2X higher throughput compared to the previous generation. When combined with NVIDIA NVSwitch™, up to 16 A100 GPUs can be interconnected at up to 600 gigabytes per second (GB/sec), unleashing the highest application performance possible on a single server. NVLink is available in A100 SXM GPUs via HGX A100 server boards and in PCIe GPUs via an NVLink Bridge for up to 2 GPUs.
MULTI-INSTANCE GPU (MIG)
MULTI-INSTANCE GPU (MIG)
An A100 GPU can be partitioned into as many as seven GPU instances, fully isolated at the hardware level with their own high-bandwidth memory, cache, and compute cores. MIG gives developers access to breakthrough acceleration for all their applications, and IT administrators can offer right-sized GPU acceleration for every job, optimizing utilization and expanding access to every user and application.
HIGH-BANDWIDTH MEMORY (HBM2E)
HIGH-BANDWIDTH MEMORY (HBM2E)
With up to 80 gigabytes of HBM2e, A100 delivers the world’s fastest GPU memory bandwidth of over 2TB/s, as well as a dynamic random-access memory (DRAM) utilization efficiency of 95%. A100 delivers 1.7X higher memory bandwidth over the previous generation.
STRUCTURAL SPARSITY
STRUCTURAL SPARSITY
AI networks have millions to billions of parameters. Not all of these parameters are needed for accurate predictions, and some can be converted to zeros, making the models “sparse” without compromising accuracy. Tensor Cores in A100 can provide up to 2X higher performance for sparse models. While the sparsity feature more readily benefits AI inference, it can also improve the performance of model training.

When to choose a GPU Server Rental A100 Hosting

The NVIDIA A100 Tensor Core GPU is the flagship product of the NVIDIA data center platform for deep learning, HPC, and data analytics. The platform accelerates over 2,000 applications, including every major deep learning framework. A100 is available everywhere, from desktops to servers to cloud services, delivering both dramatic performance gains and cost-saving opportunities.
Up to 3X Higher AI Training on Largest Models
Deep Learning Training
AI models are exploding in complexity as they take on next-level challenges such as conversational AI. Training them requires massive compute power and scalability. NVIDIA A100 Tensor Cores with Tensor Float (TF32) provide up to 20X higher performance over the NVIDIA Volta with zero code changes and an additional 2X boost with automatic mixed precision and FP16.
Up to 249X Higher AI Inference Performance Over CPUs
Deep Learning Inference
A100 introduces groundbreaking features to optimize inference workloads. It accelerates a full range of precision, from FP32 to INT4. Multi-Instance GPU (MIG) technology lets multiple networks operate simultaneously on a single A100 for optimal utilization of compute resources. And structural sparsity support delivers up to 2X more performance on top of A100’s other inference performance gains.
11X More HPC Performance in Four Years
High-Performance Computing
NVIDIA A100 introduces double precision Tensor Cores to deliver the biggest leap in HPC performance since the introduction of GPUs. Combined with 80GB of the fastest GPU memory, researchers can reduce a 10-hour, double-precision simulation to under four hours on A100. HPC applications can also leverage TF32 to achieve up to 11X higher throughput for single-precision, dense matrix-multiply operations.
2X Faster than A100 40GB on Big Data Analytics Benchmark
High-Performance Data Analytics
Data scientists need to be able to analyze, visualize, and turn massive datasets into insights. But scale-out solutions are often bogged down by datasets scattered across multiple servers. Accelerated servers with A100 provide the needed compute power—along with massive memory, over 2 TB/sec of memory bandwidth, and scalability with NVIDIA® NVLink® and NVSwitch™, —to tackle these workloads.

Dedicated NVIDIA A100 GPU Hosting Pricing

Dedicated GPU A100 pairs with Dual E5-2697v4 CPU and 256GB RAM delivering high performance for AI, data analytics, and HPC applications.
Black Friday Sale

Enterprise GPU Dedicated Server - A100

  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
1mo3mo12mo24mo
32% OFF Recurring (Was $799.00)
$ 575.00/mo

Multi-GPU Dedicated Server - 4xA100

  • 512GB RAM
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: 4 x Nvidia A100
  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
1mo3mo12mo24mo
$ 1899.00/mo

Alternatives to dedicated servers with Nvidia A100

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Nvidia Tesla A40 Hosting

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FAQ of Dedicated NVIDIA A100 GPU Server Hosting

Answers to frequently asked questions about A100 GPU dedicated Server can be found here

Is the GPU dedicated server self-managed?

Yes. But our experienced staff is always here and willing to help you with any kind of problems you may have with your rental GPU dedicated server. Please contact us online in a live chat or send us an email if you need any help.

How long will it take to set up A100 GPU dedicated servers?

We usually need 24-48 hours for preparing a GPU dedicated server.

Why is your price more affordable and cheaper than other providers?

We have been in the hosting business since 2005. This experience helps us design an economical and top-quality network as well as hardware and software infrastructure for our products. We do not provide phone support right now. It allows us to pass the savings to our clients.

Can I add more resources to my A100 GPU server?

Yes, you can add more resources or other hardware configurations, such as CPU, Disk, RAM, and bandwidth, to your A100 hosting server.

What is NVIDIA A100 used for?

NVIDIA A100 Tensor Core GPU delivers unprecedented acceleration at every scale to power the world's highest-performing elastic data centers for AI, data analytics, and HPC. Powered by the NVIDIA Ampere Architecture, A100 is the engine of the NVIDIA data center platform.

Is RTX 4090 better than A100?

The NVIDIA RTX 4090 and the NVIDIA A100 are both high-performance graphics processors, but they are designed for different purposes and target different markets.

The NVIDIA RTX series is primarily focused on gaming and consumer applications. The RTX 4090, as a hypothetical successor to the RTX 3090, would likely offer improved gaming performance, ray tracing capabilities, and AI features compared to its predecessor.

On the other hand, the NVIDIA A100 is part of the NVIDIA Ampere architecture and is tailored for data center and professional applications, such as artificial intelligence, machine learning, and high-performance computing. The A100 is optimized for heavy computational workloads and offers features like Tensor Cores and Multi-Instance GPU (MIG) technology, which make it well-suited for AI training and inference tasks.

Is Nvidia A100 good for gaming?

The NVIDIA A100 is not primarily designed for gaming purposes. It is a high-performance GPU that is optimized for data center and professional applications, such as artificial intelligence training, inference, high-performance computing, and data analytics. While it is a powerful GPU, it may not provide the same level of gaming-specific features and optimizations as GPUs in NVIDIA's GeForce RTX series.

Is Nvidia A100 good for live streaming?

The NVIDIA A100, while not specifically designed for live streaming, can still be used for that purpose due to its high computational power. For live streaming, NVIDIA's GeForce RTX series GPUs, such as the RTX 30 series, are generally more suitable. These gaming-focused GPUs offer features like dedicated hardware encoding (NVENC), which offloads the streaming workload from the CPU to the GPU, resulting in improved streaming performance and lower system impact.