Powerful Enterprise GPU Servers
Take Your Workloads and computing tasks to the Next Level with the latest A100 and A30 NVIDIA GPU Servers.
High Performance GPU Servers that can easily scale & handle all your:
Accelerate your workload with the powerful NVIDIA GPU Servers
High Performance
Cost Effective
99.99% Uptime
Secure
Dedicated GPU Hosting plans
| GPU Card | GPU Memory | vCPU | RAM | Storage | Price | |
|---|---|---|---|---|---|---|
|
NVIDIA - A100 |
40 GB |
16 |
115 GB |
1500 GB |
₹ 124100 /Mo |
|
|
NVIDIA - A100-80 |
80 GB |
16 |
115 GB |
1500 GB |
₹164980 /mo |
|
|
NVIDIA - A100-80 |
80 GB |
16 |
115 GB |
250 GB |
₹164980 /mo |
|
|
NVIDIA - 2XA100 |
80 GB |
32 |
230 GB |
3000 GB |
₹248200 /mo |
|
|
NVIDIA - 2XA100-80 |
160 GB |
32 |
230 GB |
3000 GB |
₹ 329960 /mo |
All Plans include
Custom Configuration
Fast Network
Support
Reliability
Advanced Architecture
Dedicated IPV4
Need Custom Solution
Plan your project needs upfront, and send us a note using the form and someone from the ServerGuy team will be in touch.
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Why you need a GPU Server?
GPU Servers, a.k.a Graphical Processing Unit is commonly known as graphic cards. They are designed to run powerful algorithms parallelly, so the CPU can be free of such tasks. For more details, you can refer to our CPU vs GPU vs TPU article.
CPU is a central processing unit, and they serve multiple purposes but mostly do general computing tasks. With few cores focusing on sequential processing, CPU is not optimized for running parallel tasks. GPU is composed of thousands of small cores that can run thousands of functions at the same time.
GPUs are used for tasks such as Artificial Intelligence (AI), Machine Learning, Deep Learning, Big Data manipulation, 3D rendering, and even streaming, demanding the power that can maintain a large number of requests & instructions at one time.Â
Popular Use Cases Where High End GPU Required
Large Data Processing
There is lots of data, and every organization is looking to make sense and use it for optimal profit. Data processing has become an essential aspect of every business sector.
A powerful GPU is the primary requirement for playing with the data. There, GPU enhances the CPU’s power and performance and enable them to do high-end calculations.
With a Graphic card, the CPU can execute parallel tasks at a much faster speed. The use of high-end GPU Servers among data scientists is increasing day by day, as it powers their infrastructure and accelerates the training of their model.
Rendering
Dedicated Server is an ideal combination for post-processing videos, images, or 3D models. Rendering each pixel on the screen could take time with a CPU, but when accompanied by the GPU rendering servers, the image/video would parse quickly.
The process of rendering the scenes with a large number of elements can lag even with the powerful high CPU servers, as they are not built to manage such tasks.
A high end scalable GPU servers would give the power to the machine to smoothen the flow of parallel video streams, and execution of multiple components at the same time.
Streaming
GPUs are excellent solutions for online streaming. Running webinars, live programs, and streaming worldwide –are high computing tasks and require powerful machines.
We can help you in streaming the online event to a large number of visitors, delivering high-quality content to all parallel connections flawlessly.
Optimize Workflows and Drive Innovation with Enterprise GPU Servers
Frequently Asked questions​
NVIDIA® Tesla® A100 GPU Servers are the most advanced GPU servers. With latest architecture from NVIDIA, it offers performance that is equivalent to 100 CPUs.Â
You can add up to 4 to 8 GPUs to a server; however, adding 16 GPUs to a single server creates complications. Tesla V100 is a modern GPU and one of the best in the market.Â
To add more than 8 GPUs, you will need more servers. Contact our team for further details.
CPUs are not built to run lots of tasks at the same time. They focus on one task at a time and then assign the task in sequence on a priority basis. It runs several tasks simultaneously and will lag the CPU, and the performance will be low.Â
By adding a GPU server, you can distribute the demanding tasks to the GPU, and the CPU resources will remain free. The combination effectively reduces the CPU workload, and the GPU can escalate the computing task much faster.
No.
But you can request your requirements, and our technical team will arrange it.
Virtualized GPU Servers do have their advantage, but it comes at the cost of reduced performance.
Our technical team is just an email away from you. If you face any issues or require any technical assistance, you can contact us at any time at support@serverguy.com.
NVIDIA has combined a 40 GB HBM2e memory module with the A100 SXM4 40 GB GPU, establishing connectivity through a 5120-bit memory interface. The GPU operates at a base frequency of 1095 MHz, with the capability to boost up to 1410 MHz. Simultaneously, the memory operates at a frequency of 1215 MHz.
