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
| Flavor ID | Server Config | GPUs | GPU Memory | Infiniband Interconnect (Gbit/s) | Quantity | Price | |
|---|---|---|---|---|---|---|---|
|
bm3-ai-large-a100-40-4 |
2 Intel Xeon 8468 / 2 TB RAM / 8×3.84 TB NVMe / 4x Nvidia A100 / 8x200G Infiniband |
4xA100 |
40GB |
200 |
1 |
$16.46 /1 hour
$11,851.85 /1month
$135,111.09 / 1 year |
|
|
bm3-ai-large-a100-0-4 |
2 Intel Xeon 8468 / 2 TB RAM / 8×3.84 TB NVMe / 4x Nvidia A100 / 8x200G Infiniband |
8xA100 |
80 GB |
800 |
1 |
$ 17.479 / 1 hour
$ 12,584.95 / 1 month
$ 143,468.43 / 1 year |
|
|
bm3-ai-large-a100-0-4 |
2 Intel Xeon 8468 / 2 TB RAM / 8×3.84 TB NVMe / 4x Nvidia A100 / 8x200G Infiniband |
8xH100 |
80 GB |
1600 |
1 |
$ 31.828 / 1 hour
$ 22,916.04 / 1 month
$ 261,242.86 / 1 year |
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.
Dedicated bare metal GPU servers or virtual GPU instances?
Сhoose what works for you!
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.
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
AI IPU
Bare metal servers
Virtual machines
Managed Kubernetes
Frequently Asked Questions
Contact our sales team at sales@computer.com 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.
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
