GPU-accelerated ECSs provide outstanding floating-point computing capabilities. They are suitable for applications that require real-time, highly concurrent massive computing.
GPU-accelerated ECSs are classified as G series and P series of ECSs.
Recommended: Computing-accelerated P2s
Helpful links:
Type | Series | Supported Image |
|---|---|---|
Graphics-accelerated | G5 |
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Computing-accelerated | P3 |
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Computing-accelerated | P2s |
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Overview
G5 ECSs use NVIDIA GRID vGPUs and provide comprehensive, professional graphics acceleration. They use NVIDIA Tesla V100 GPUs and support DirectX, OpenGL, and Vulkan. These ECSs provide 16 GiB of GPU memory, meeting requirements from entry-level through professional graphics processing.
Select your desired GPU-accelerated ECS type and specifications.
Specifications
Flavor | vCPUs | Memory (GiB) | Max./Assured Network Bandwidth (Gbit/s) | Max. Network PPS (10,000) | Max. NIC Queues | GPUs | GPU Memory (GiB) | Virtualization |
|---|---|---|---|---|---|---|---|---|
g5.8xlarge.4 | 32 | 128 | 25/15 | 200 | 16 | 1 × V100 | 16 | KVM |
g5.16xlarge.4 | 64 | 256 | 30/30 | 400 | 32 | 2 × V100 | 2 × 16 | KVM |
G5 ECS Features
Supported Software
G5 ECSs are used in graphics acceleration scenarios, such as video rendering, cloud desktop, and 3D visualization. If the software relies on GPU DirectX and OpenGL hardware acceleration, use G5 ECSs. G5 ECSs support the following commonly used graphics processing software:
Notes
Resources will be released after a G5 ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop it.
For details about how to configure a GRID license, see Manually Installing a GRID Driver on a GPU-accelerated ECS.
For details, see Manually Installing a GRID Driver on a GPU-accelerated ECS.
Overview
P3 ECSs use NVIDIA A100 GPUs and provide flexibility and ultra-high-performance computing. P3 ECSs have strengths in AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics. Theoretically, the FP32 is 19.5 TFLOPS, and the TF32 tensor core is 156 TFLOPS | 312 TFLOPS (sparsity enabled).
Specifications
Flavor | vCPUs | Memory (GiB) | Max./Assured Network Bandwidth (Gbit/s) | Max. Network PPS (10,000) | Max. NIC Queues | Max. NICs | GPUs | GPU Memory (GiB) | Virtualization |
|---|---|---|---|---|---|---|---|---|---|
p3.2xlarge.8 | 8 | 64 | 10/4 | 100 | 4 | 4 | 1 × NVIDIA A100 80 GB | 80 | KVM |
p3.4xlarge.8 | 16 | 128 | 15/8 | 200 | 8 | 8 | 2 × NVIDIA A100 80 GB | 160 | KVM |
p3.8xlarge.8 | 32 | 256 | 25/15 | 350 | 16 | 8 | 4 × NVIDIA A100 80 GB | 320 | KVM |
p3.16xlarge.8 | 64 | 512 | 36/30 | 700 | 32 | 8 | 8 × NVIDIA A100 80 GB | 640 | KVM |
P3 ECS Features
Similar to other types of ECSs, P3 ECSs can be provisioned in a few minutes.
The supercomputing ecosystem allows you to build up a flexible, high-performance, cost-effective computing platform. A large number of HPC applications and deep-learning frameworks can run on P3 ECSs.
Supported Software
P3 ECSs are used in computing acceleration scenarios, such as deep learning training, inference, scientific computing, molecular modeling, and seismic analysis. If the software is required to support GPU CUDA parallel computing, use P3 ECSs. P3 ECSs support the following commonly used software:
Notes
Resources will be released after a P3 ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop it.
Overview
P2s ECSs use NVIDIA Tesla V100 GPUs to provide flexibility, high-performance computing, and cost-effectiveness. P2s ECSs provide outstanding general computing capabilities and have strengths in fields such as AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics.
Specifications
Flavor | vCPUs | Memory (GiB) | Max./Assured Network Bandwidth (Gbit/s) | Max. Network PPS (10,000) | Max. NIC Queues | Max. NICs | GPUs | GPU Connection | GPU Memory (GiB) | Virtualization |
|---|---|---|---|---|---|---|---|---|---|---|
p2s.2xlarge.8 | 8 | 64 | 10/4 | 50 | 4 | 4 | 1 × V100 | PCIe Gen3 | 1 × 32 GiB | KVM |
p2s.4xlarge.8 | 16 | 128 | 15/8 | 100 | 8 | 8 | 2 × V100 | PCIe Gen3 | 2 × 32 GiB | KVM |
p2s.8xlarge.8 | 32 | 256 | 25/15 | 200 | 16 | 8 | 4 × V100 | PCIe Gen3 | 4 × 32 GiB | KVM |
p2s.16xlarge.8 | 64 | 512 | 30/30 | 400 | 32 | 8 | 8 × V100 | PCIe Gen3 | 8 × 32 GiB | KVM |
P2s ECS Features Similar to other types of ECSs, P2s ECSs can be provisioned in a few minutes. The supercomputing ecosystem allows you to build up a flexible, high-performance, cost-effective computing platform. A large number of HPC applications and deep-learning frameworks can run on P2s ECSs.
Supported Software
P2s ECSs are used in computing acceleration scenarios, such as deep learning training, inference, scientific computing, molecular modeling, and seismic analysis. If the software is required to support GPU CUDA parallel computing, use P2s ECSs. P2s ECSs support the following commonly used software:
Notes Resources will be released after a P2s ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop it.