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.
High performance
It utilizes several parallel slave nodes to enhance the computing power of requesting tasks.
Load balancing
It increases compute workloads across all slave nodes to simply manage different types of work.
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
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
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
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
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
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
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
- 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.
Software and Compatibility
Opt for service providers that offer pre-installed and enhanced software for your requirements. Make sure that you can simply install and arrange your own software according to you.
Hardware Specifications
Check the variety of GPU dedicated servers available (like A100, etc.). Ensure they are appropriate for your heavy workload, whether it is graphic rendering or scientific simulations.
Pricing Model
Properly understand the available pricing model and then simply compare the prices relative to the high-performance and proficiencies that are accessible. Clearly check for all hidden charges.
Network and Connectivity
Good-speed internet connections and decreased latency network connections are very important for transferring all big datasets and guaranteeing successful GPU communication.
Customer Support
Make sure that there is customer support available all the time to help you with any occurred problem. Properly check the uptime guarantees and scalability options of the service provider.
Scalability
See if the service provider lets you level up or down according to your project requirements. Ensure that the service provider can manage all the variations in the tasks, from small tests to big tests.
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.
Cost Efficiency
Quicker complex computation times state that the work can be done with more speed, decreasing all other unnecessary costs.
Improved Reliability and Redundancy
Best-quality clusters often have unique features for fault acceptance and decrement that help tasks run easily.
Accelerated Computation
Clusters can easily process large datasets, making them appropriate for various tasks such as image or video processing, etc.
