High availability
The GPU cluster reroutes requests to different nodes in the event of a failure.
Nvidia GPU Cluster for Deep Learning and HPC
Maximize your deep learning and HPC capabilities with Nvidia GPU Cluster. Harness the power of our innovative technology for superior computing performance.
Rent HPC GPU Servers for Building Your GPU Cluster
Looking to create a powerful GPU cluster? Rent HPC GPU servers from us and supercharge your computing capabilities now!
Advanced GPU Dedicated Server - A4000
$ 279.00/mo
1mo3mo12mo24mo
Order Now- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A4000
- Microarchitecture: Ampere
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
Advanced GPU Dedicated Server - V100
$ 229.00/mo
1mo3mo12mo24mo
Order Now- 128GB RAM
- Dual 12-Core E5-2690v3
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia V100
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
- Cost-effective for AI, deep learning, data visualization, HPC, etc
Half-Year Pay, Full-Year Deal
Advanced GPU Dedicated Server - A5000
$ 191.90/mo
45% OFF Recurring (Was $349.00)
1mo3mo12mo24mo
Order Now- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A5000
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Enterprise GPU Dedicated Server - RTX 4090
$ 409.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 4090
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
Enterprise GPU Dedicated Server - RTX A6000
$ 409.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Enterprise GPU Dedicated Server - A40
$ 439.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A40
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPS
- Ideal for hosting AI image generator, deep learning, HPC, 3D Rendering, VR/AR etc.
Multi-GPU Dedicated Server - 3xV100
$ 469.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 3 x Nvidia V100
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
- Expertise in deep learning and AI workloads with more tensor cores
Multi-GPU Dedicated Server - 3xRTX A5000
$ 539.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 3 x Quadro RTX A5000
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Enterprise GPU Dedicated Server - A100
$ 639.00/mo
1mo3mo12mo24mo
Order Now- 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 HBM2
- FP32 Performance: 19.5 TFLOPS
- Good alternativeto A800, H100, H800, L40. Support FP64 precision computation, large-scale inference/AI training/ML.etc
Multi-GPU Dedicated Server- 2xRTX 4090
$ 729.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 2 x GeForce RTX 4090
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
Multi-GPU Dedicated Server - 3xRTX A6000
$ 899.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 3 x Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
New Arrival
Multi-GPU Dedicated Server- 2xRTX 5090
$ 859.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual Gold 6148
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 2 x GeForce RTX 5090
- Microarchitecture: Ada Lovelace
- CUDA Cores: 20,480
- Tensor Cores: 680
- GPU Memory: 32 GB GDDR7
- FP32 Performance: 109.7 TFLOPS
Multi-GPU Dedicated Server - 4xRTX A6000
$ 1199.00/mo
1mo3mo12mo24mo
Order Now- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 4 x Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Multi-GPU Dedicated Server - 4xA100
$ 1899.00/mo
1mo3mo12mo24mo
Order Now- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 4 x Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
Multi-GPU Dedicated Server - 8xRTX A6000
$ 2099.00/mo
1mo3mo12mo24mo
Order Now- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 8 x Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
What's GPU Cluster?
GPU cluster typically refers to a collection of interconnected computers, each equipped with one or more GPUs, working together as a unified system. These systems often run on specialized software, such as HPC (High-Performance Computing) clusters, which facilitate distributed computing tasks. Clusters are generally designed for diverse workloads and research projects, where individual nodes can be dedicated to specific tasks or run different parts of a larger computation concurrently. They often operate within a managed environment, offering advanced scheduling and resource management capabilities.
There are three main advantages of GPU cluster:
High performance
The GPU cluster uses multiple parallel slave nodes to increase compute power for more demanding tasks.
Load balancing
The GPU cluster spreads compute workloads evenly across slave nodes to handle a large volume of jobs.
How to Choose GPU Cluster Hosting
Choosing a GPU cluster hosting provider requires careful consideration of several factors to ensure that it meets your specific needs for performance, scalability, and cost-effectiveness. Here are some key points to consider:
Hardware Specifications
Verify the types of GPUs offered (e.g., NVIDIA A100, V100, RTX 4090). Ensure they are suitable for your workload, whether it be deep learning, rendering, or scientific computing.
Scalability
Check if the provider allows you to scale up or down based on your project needs. Ensure the provider can handle the scale of your projects, from small tests to large-scale deployments.
Network and Connectivity
High-speed internet and low-latency network connections are crucial for transferring large datasets and ensuring efficient GPU communication.
Software and Compatibility
Look for providers that offer pre-installed and optimized software for your specific needs. Ensure you can install and configure your own software as needed.
Cost and Pricing Model
Understand the pricing model and compare the cost relative to the performance and capabilities offered. Look for any hidden fees.
Customer Support
Ensure there is 24/7 customer support available to assist with any issues. Check the uptime guarantees and reliability promises made by the provider.
Accelerated Computation
GPU clusters can process large datasets quickly, making them suitable for tasks like image and video processing, big data analytics, and more.
Scalability
Multiple GPUs can be pooled together to tackle large computational problems, allowing for efficient use of resources.
Cost Efficiency
Faster computation times mean projects can be completed more quickly, reducing overall costs.
Improved Reliability and Redundancy
High-quality GPU clusters often include features for fault tolerance and redundancy, ensuring that tasks can continue to run smoothly even if some components fail.
Benefits of Using GPU Cluster
Using a GPU cluster provides numerous benefits, particularly for tasks that require significant computational power such as deep learning, scientific simulations, and 3D rendering. Here are some key benefits of using a GPU cluster:
GPU Cluster vs GPU Farm
| Features | GPU Cluster | GPU Farm |
|---|---|---|
| Architecture | Simple, concise, readable | Not easy to use |
| Nodes | Highly integrated, tightly interconnected GPU nodes | Distributed, independent GPU computing resources |
| Management | Unified management system (such as Slurm, Kubernetes) | Batch processing system or cloud management platform |
| Interconnection | High-speed network interconnection | General network interconnection |
| Task type | Highly parallel computing tasks, such as scientific computing and deep learning training | Distributed rendering, data mining, batch processing tasks |
| Scalability | Easy to expand by adding nodes | More independent GPUs can be added, but there may be no cluster coordination |
| Typical applications | Supercomputing centers, technology companies | Animation studios, video production companies |
Faqs of Nvidia GPU Clusters
Frequently Asked Questions about Nvidia Clusters (GPU-Enabled Clusters)
What is Nvidia cluster?
An NVIDIA cluster refers to a group of computers or servers that are networked together and equipped with NVIDIA GPUs (Graphics Processing Units) to perform high-performance computing tasks.
How to build a GPU cluster?
Building a GPU cluster involves several steps, from planning the hardware and network infrastructure to configuring the software and deploying the system.
What are A100 clusters used for?
NVIDIA A100 clusters are used for a wide range of high-performance computing (HPC) applications due to their exceptional processing power, memory bandwidth, and versatility.
When do you need to build a GPU cluster for AI?
Building a GPU cluster for AI can provide significant benefits when you have specific computational needs that exceed the capabilities of individual GPUs or standard computing environments. It is beneficial when dealing with large-scale, complex models and datasets, requiring scalable and efficient computational resources. It supports high-throughput, real-time applications, and enables cutting-edge research and rapid development.
What is the difference between HPC cluster and GPU cluster?
In HPC clusters, CPUs are ideally suited for serial instruction processing. GPUs are not suitable for serial instruction processing, and slow down algorithms requiring serial execution compared to CPUs. CPUs come with large local cache memory which empowers them to handle multiple sets of linear instructions.
Contact Us Now
Contact us now to learn more about our GPU cluster, and let our high-performance GPU solutions propel your business forward!