Graphics servers with Tesla V100
All graphics servers with Tesla V100 are based on two Intel® Xeon® Gold 2nd generation 6240R CPUs with a base clock speed of 2.4 GHz and a maximum clock speed with Turbo Boost technology of 4.0 GHz.
Each processor contains two Intel® AVX-512 units and supports Intel® AVX-512 Deep Learning Boost functions. This set of instructions speeds up multiplication and addition operations with reduced accuracy, which are used in many internal cycles of the deep learning algorithm.
Each server has up to 3072 GB of DDR4 ECC Reg 2993 MHz RAM. Local storage with a total capacity of 1920 GB is organized on Intel® solid-state drives, designed specifically for data centers.
GPU Tesla V100
Equipped with 640 Tensor Cores, the Tesla V100 is the first accelerator to overcome the performance barrier of 100 teraoperations per second (TOPS) in deep learning tasks. Models that took weeks and months to train on previous generation systems can now be trained in just a few days.
High Performance Computing (HPC) is the fundamental pillar of modern science. From weather forecasting and the creation of new medicines to the search for energy sources, scientists constantly employ large computing systems to model our world and predict events within it. AI expands the capabilities of HPC, enabling scientists to analyze vast amounts of data and extract useful information where simulations alone cannot provide a complete picture of what is happening.
The Tesla V100 graphics accelerator is designed to provide a fusion of HPC and AI. This is a solution for HPC systems, which will prove itself adept in computing for simulations and data processing for extracting useful information from them. By combining CUDA and Tensor Cores in one architecture, a server equipped with Tesla V100 graphics accelerators can augment or replace hundreds of traditional CPU servers, performing both traditional HPC and AI tasks. Now, every scientist can access a supercomputer to tackle the most challenging problems.
| Video memory capacity |
32 GB |
| Type of video memory |
HBM2 (ECC) |
| Memory bandwidth |
900 Gb/s |
| Tensor cores |
640 |
| CUDA cores |
5120 |
GPU performance benchmarks
Performance benchmarks results in a virtual environment for 1 Tesla V100 graphics card.
-
OctaneBench 2020
pts
-
Matrix multiply example
GFlop/s
-
Hashcat bcrypt
H/s
100% Performance
Each physical core or GPU adapter is dedicated to a single client.
This means:
- 100% vCPU time is available;
- Physical pass-through of GPUs inside virtual servers;
- Reduced storage and network load on hypervisors, delivering more storage and network performance to clients.
Up to 75,000 IOPS1 for RANDOM READ and up to 20,000 IOPS for RANDOM WRITE on Virtual Servers with local SSDs
Up to 70 000 IOPS1 for RANDOM READ and up to 60 000 IOPS for RANDOM WRITE on Virtual Servers with block storage volumes
You can be confident that Virtual Servers do not share vCPU or GPU resources with one another.
Ready-made OS images with the required software
Create virtual servers by utilizing our pre-configured OS images with either Windows or Linux, along with specialized pre-installed software.
View all the pre-installed images
in the Marketplace.
Pure OpenStack API
Developers and system administrators can manage the cloud using the full OpenStack API.
Authenticate ninja_user example:
$ curl -g -i -X POST https://api.immers.cloud:5000/v3/auth/tokens \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "User-Agent: YOUR-USER-AGENT" \
-d '{"auth": {"identity": {"methods": ["password"], "password": {"user": { "name": "ninja_user", "password": "ninja_password", "domain": {"id": "default"}}}}, "scope": {"project": {"name": "ninja_user", "domain": {"id": "default"}}}}}'
Create ninja_vm example:
$ curl -g -i -X POST https://api.immers.cloud:8774/v2.1/servers \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "User-Agent: YOUR-USER-AGENT" \
-H "X-Auth-Token: YOUR-API-TOKEN" \
-d '{"server": {"name": "ninja_vm", "imageRef": "8b85e210-d2c8-490a-a0ba-dc17183c0223", "key_name": "mykey01", "flavorRef": "8f9a148d-b258-42f7-bcc2-32581d86e1f1", "max_count": 1, "min_count": 1, "networks": [{"uuid": "cc5f6f4a-2c44-44a4-af9a-f8534e34d2b7"}]}}'
STOP ninja_vm example:
$ curl -g -i -X POST https://api.immers.cloud:8774/v2.1/servers/{server_id}/action \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "User-Agent: YOUR-USER-AGENT" \
-H "X-Auth-Token: YOUR-API-TOKEN" \
-d '{"os-stop" : null}'
START ninja_vm example:
$ curl -g -i -X POST https://api.immers.cloud:8774/v2.1/servers/{server_id}/action \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "User-Agent: YOUR-USER-AGENT" \
-H "X-Auth-Token: YOUR-API-TOKEN" \
-d '{"os-start" : null}'
SHELVE ninja_vm example:
$ curl -g -i -X POST https://api.immers.cloud:8774/v2.1/servers/{server_id}/action \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "User-Agent: YOUR-USER-AGENT" \
-H "X-Auth-Token: YOUR-API-TOKEN" \
-d '{"shelve" : null}'
DELETE ninja_vm example:
$ curl -g -i -X DELETE https://api.immers.cloud:8774/v2.1/servers/{server_id} \
-H "User-Agent: YOUR-USER-AGENT" \
-H "X-Auth-Token: YOUR-API-TOKEN"
Documentation
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