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GPU Cluster for HPC and Deep Learning

With GPU Cluster, you can improve your HPC and deep learning skills. Get the complete power of our advanced technology for outstanding computing performance.

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What is a GPU Cluster?

It mainly denotes a collection of several interlinked GPU dedicated servers, each one fully equipped with a single or more than that GPU, working with each other as a single system.

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High performance

It utilizes several parallel slave nodes to enhance the computing power of requesting tasks.

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Load balancing

It increases compute workloads across all slave nodes to simply manage different types of work.

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High availability

It simply reroutes all generated requests to a variety of nodes in the case of any specific failure.

Tailored Pricing Plans for Your Ideal GPU Cluster Setup

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A4000

$ 349.00/month

$50.99
  • Dual 12-Core E5-2697v2
  • 128GB RAM
  • 2TB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia Quadro RTX A4000
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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V100

$ 669.00/month

$50.99
  • Multi-GPU - 3xV100
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: 3 x Nvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 3
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Save 40%
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RTX 4090

$ 455.00/month

$50.99
  • Enterprise GPU - RTX 4090
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: GeForce RTX 4090
  • Microarchitecture: Ada Lovelace
  • Max GPUs: 1
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Save 40%
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A40

$ 619.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: Nvidia A40
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 37.48 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Save 40%
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Multiple GPU - 3xV100

$ 719.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe
  • 1Gbps Port Speed
  • GPU: 3xNvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 3
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Save 40%
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Multiple GPU - 3xRTX A5000

$ 819.0099/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe
  • 1Gbps Port Speed
  • GPU: 3xQuadro RTX A5000
  • Microarchitecture: Ampere
  • Max GPUs: 3
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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  • Instant Support
  • Quick Deploy
  • Robust Security

How to Select GPU Cluster Hosting

While selecting the best GPU dedicated servers, you need proper consideration of various factors to make sure that
it fulfills your requirements for high performance, reliability, and affordability.

Buy GPU Cluster for High-Performance Computing

Get unmatched performance with our GPU clusters. Our GPU clusters offer the scalability and power that your projects need and are enhanced for demanding workloads like artificial intelligence, machine learning, and 3D graphic rendering.

Advantages of Using GPU Cluster

A GPU cluster offers a robust acceleration in computational power by using the proficiencies of various GPUs together. This whole setup helps in managing those tasks that need robust parallel processing.

Utilizing a cluster offers a lot of advantages, especially for various tasks that need high-level computational power, like scientific computations and 3D modeling. Here are a few advantages of using a cluster:

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Scalability

Several GPU dedicated servers can be connected together to handle complex computations, letting productive use of all resources.

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Cost Efficiency

Quicker complex computation times state that the work can be done with more speed, decreasing all other unnecessary costs.

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Improved Reliability and Redundancy

Best-quality clusters often have unique features for fault acceptance and decrement that help tasks run easily.

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Accelerated Computation

Clusters can easily process large datasets, making them appropriate for various tasks such as image or video processing, etc.

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Talk to our experts 24/7 to resolve your issues on time.

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Frequently Asked Questions

It is a group of multiple computers that have a single GPU on every single node. Various GPU dedicated servers offer high computing power for several complex computational tasks, like video and image processing and training AI models and several other ML algorithms.

Linking various GPU dedicated servers from several different nodes into a single cluster makes it simply possible to run AI inference with reduced latency. This is just because every single node can make outcomes locally without having to connect with a remote or the cloud data center.

The NVIDIA A100 GPU dedicated servers are one of the best choices for performing AI model training, with enhanced memory and good computing power. It includes MIG technology, which helps you to divide a particular GPU into almost 7 smaller instances.

Its setup consists of several connected servers or systems; each of them is armed with a single or more GPU-dedicated servers, working with each other to perform parallel computing. All these nodes are connected with the help of a high-speed network to enable quick data sharing. Cluster management software easily synchronizes task planning along with resource sharing, while other important frameworks such as TensorFlow allow distributed processing. Optimal performance storage solutions manage big datasets and provide proper cooling.

Having numerous GPU dedicated servers in a single cluster allows quick parallel processing of all tasks, significantly enhancing computational productivity for those tasks that can be segmented and processed at the same time.

GPUs have more cores, but these are very less productive and provide less accuracy as compared to CPU cores. In the case of HPC clusters, CPUs are an appropriate choice for sequential task processing. GPUs are not the best option for serial processing tasks, and slow down all algorithms that need serial processing.

The A100 is an ideal option for the NVIDIA GPU dedicated servers, appropriate for data centers and advanced computing. It is a tensor-core based GPU for AI/ML and powering all applications. Organizations also utilize it for running deep learning and artificial intelligence research and development-related tasks.

CPUs are specially developed to manage a variety of complex computational tasks sequentially. Where GPUs outshine at processing several basic tasks in parallel.

Building a GPU cluster consists of selecting the right GPUs, setting up high-speed networking, and configuring compatible hardware. Install needed software such as clustering and CUDA tools, then enhance performance for demanding workloads such as AI/ML, etc. Lastly, test and manage as required.

GPU clusters are utilized especially for high-level tasks such as AI model training, 3D graphic rendering, complex simulations, and cryptocurrency mining, providing exceptional performance for high-level parallel processing.

The Colossus supercomputer is the world’s largest GPU cluster, developed by NVIDIA and xAI, presenting almost 100,000 NVIDIA Hopper GPUs (scaling up to 200,000). It is engineered for AI-based model training and generative AI enhancements with high performance and productivity.