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AI GPU Cloud Infrastructure

Computer.Com bare metal servers and virtual machines powered by NVIDIA A100 and H100 GPUs. Boost the productivity of your AI tasks with breakthrough performance!

Configurations and prices

Bare Metal
Flavor ID Server Config GPUs GPU Memory Infiniband Interconnect (Gbit/s) Quantity Price     
2 Intel Xeon 8468 / 2 TB RAM / 8x3.84 TB NVMe / 4x Nvidia A100 / 8x200G Infiniband
$16.46 /1 hour $11,851.85 /1month $135,111.09 / 1 year
2 Intel Xeon 8468 / 2 TB RAM / 8x3.84 TB NVMe / 4x Nvidia A100 / 8x200G Infiniband
80 GB
$ 17.479 / 1 hour $ 12,584.95 / 1 month $ 143,468.43 / 1 year
2 Intel Xeon 8468 / 2 TB RAM / 8x3.84 TB NVMe / 4x Nvidia A100 / 8x200G Infiniband
80 GB
$ 31.828 / 1 hour $ 22,916.04 / 1 month $ 261,242.86 / 1 year
Virtual instances
Flavor IDServer configGPUsGPU MemoryInfiniband Interconnect (Gbit/s)QuantityPrice
g3-ai-24-232-1100-a100-80-124 vCPU / 232 GB RAM /1100 GB NVMe / A100-1GPU1xA10080 GB800
$ 1.43 / 1 hour
$ 1,030 / 1 month
$ 12,356 / 1 year
g3-ai-48-464-2200-a100-80-248 vCPU / 464 GB RAM /2200 GB NVMe / A100-2GPU2xA10080 GB800
$ 2.86 / 1 hour
$ 2,060 / 1 month
$ 24,711 / 1 year
g3-ai-96-1856-8800-a100-80-896 vCPU / 1856 GB RAM /8800 GB NVMe / A100-8GPU8xA10080 GB800
$ 11.44 / 1 hour
$ 8,352 / 1 month
$ 92,506 / 1 year
g3-ai-24-232-1100-h100-80-124 vCPU / 232 GB RAM /1100 GB NVMe / H100-1GPU1xH10080 GB1600
$ 2.31 / 1 hour
$ 1,664 / 1 month
$ 19,959 / 1 year
g3-ai-48-464-2200-h100-80-248 vCPU / 464 GB RAM /2200 GB NVMe / H100-2GPU2xH10080 GB1600
$ 4.62 / 1 hour
$ 3,327 / 1 month
$ 39,917 / 1 year
g3-ai-96-1856-8800-h100-80-896 vCPU / 1856 GB RAM /8800 GB NVMe / H100-8GPU8xH10080 GB1600
$ 18.48 / 1 hour
$ 13,491 / 1 month
$146,468 / 1 year
($2.09 per H100)          

Designed for AI and compute-intensive workloads

AI training

With thousands of processing cores, a graphics processing unit (GPU) can perform multiple matrix operations and calculations in parallel. As a result, GPUs complete AI training tasks much faster than traditional CPUs.

Deep learning

GPUs easily handle the high computational demands of deep neural networks and ​​recurrent neural networks, which are fundamental to developing complex deep learning models, including generative AI.

High-performance computing

Superior GPU performance is well suited for compute-intensive workloads, including dynamic programming algorithms, video rendering, and scientific simulations.

Data analytics

PUs provide high memory bandwidth and efficient data transfer capabilities. This improves the processing and manipulation of large data sets, enabling faster analysis.

GPU champs

The NVIDIA A100 and latest H100 GPUs are at the forefront of the enterprise GPU market. Both are powerful and versatile accelerators for a wide range of AI and high-performance computing (HPC) workloads.


A100 specs

  • Up to 249x higher AI inference performance over CPUs
  • Up to 20x higher performance than the previous generation of the NVIDIA GPU, V100
  • Tensor Core 3rd generation
  • Up to 80GB of HBM2e memory

H100 specs

  • Up to 4x higher performance than the A100 GPU for AI training on GPT-3
  • ​​Up to 7x higher performance than the A100 GPU for HPC applications
  • Tensor Core 4th generation
  • Up to 100GB of HBM3 memor

Prior to the H100 release in 2022, A100 was a leading GPU platform in the MLPerf industry benchmarks.


In the latest MLPerf benchmark, H100 showed better performance than competitors.

h100 (1)

Dedicated bare metal GPU servers or virtual GPU instances?

Сhoose what works for you!

Bare metal GPU servers

Bare metal servers provide direct access to the physical hardware, including the GPU. This means that all GPU resources are dedicated to you. Bare metal GPU gives you optimal performance for AI and compute-intensive workloads.

Virtual GPU instances

For the same configuration, GPUs on VMs may perform slightly slower than those on bare metal servers. But VMs offer easier management, scalability, and lower prices than bare metal GPU servers.

Managed Kubernetes with GPU worker nodes

Features like autoscaling and autohealing make Kubernetes ideal for dynamic workloads, including machine learning, video processing, and other compute-intensive tasks. With Computer.Com’s Managed Kubernetes, you can use Bare Metal and VMs with GPU as worker nodes (A100 and H100.) Simply utilize GPUs in your containers by requesting the custom GPU resource, just like you would request CPU or memory.


Take advantage of

Computer.Com Cloud solutions


Use Computer.Com’s AI cloud infrastructure powered by Graphcore IPUs to accelerate machine learning.

Bare metal servers

Deploy resource-intensive applications and services on high-performance physical servers.

Virtual machines

Leverage production-grade VMs designed for a wide range of workloads and predictable performance.

Managed Kubernetes

Provision, manage, and scale Kubernetes clusters with 99.9% SLA and support for bare metal nodes.

Frequently Asked Questions

What is a graphics processing unit?
A graphics processing unit (GPU) is a specialized electronic circuit designed to improve the rendering of computer graphics. GPUs are used in various applications, including video games, 3D modeling, and AI training. GPUs are designed for parallel processing, which means that they can execute multiple instructions at the same time. This is the main difference between GPUs and central processing units (CPUs); the latter executes instructions one at a time.
How will I be charged for GPU instances?
You will be charged for a specific configuration that you choose. If you purchase a separate GPU instance that is not part of a Kubernetes cluster, you will be charged for the corresponding VM or bare metal configuration. See the Configuration and pricing section above to learn more about our pricing.
How can I change the configuration of a GPU instance?

Contact our sales team at with your desired new instance configuration. If you need help choosing a configuration, they’ll get back to you with the best solution for your request.

Are your GPU instances fully dedicated or shared?
It depends on the type of instances you choose, bare metal or VMs. If you choose a bare metal server, all of its resources are dedicated to you. If you choose a VM, you get virtual computing resources, including those of a GPU. The physical resources of the instance (server) are shared, but the virtual resources are not. You get access to the full amount of resources that you purchased.
When will the instance I purchased be available?

After you purchase the GPU instance, it is up and running:

  • Within 3–5 minutes if it is a virtual machine
  • Within 15–20 minutes if it is a bare metal server
Do you offer a free trial period to test a GPU instance?
Yes. Fill out this form, and our sales team will contact you to discuss this option. Please note that at the end of your trial period, you will be switched to the standard pay-as-you-go plan.
Can I get custom pricing?
Yes. Fill out this form and our sales team will contact you to discuss this option.
Do you have a waiting list for GPU instances?
Yes. Fill out this form and our sales team will contact you to discuss the details and add you to our waiting list.

Contact us to get a personalized offer

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