Fastest And Best GPU SERVERS PROVIDER
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.
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Nvidia A100 GPU Hosting Server Features
Hosted dedicated servers with A100 graphics delivers superior performance over integrated graphics.
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
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
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.
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 SPARSIT
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.
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.
FAQ of Dedicated NVIDIA A100 GPU Server Hosting
Answers to frequently asked questions about A100 GPU dedicated Server can be found here
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.
We usually need 24-48 hours for preparing a GPU dedicated server.
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.
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?
Some common GPUs used in hosting environments include Nvidia GeForce, Quadro, Tesla, and RTX Server. The specific GPU you choose will depend on your needs and the applications you will be running.
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.