This page describes the features and instance types of the following vGPU-accelerated instance families in Elastic GPU Service (EGS):
sgn8ia: Latest-generation vGPU instances powered by NVIDIA Lovelace GPUs and AMD Genoa processors
sgn7i-vws: Cost-efficient vGPU instances with shared CPUs and NVIDIA A10 GPUs
vgn7i-vws: Dedicated-CPU vGPU instances with NVIDIA A10 GPUs
vgn6i-vws: Previous-generation vGPU instances with NVIDIA T4 GPUs (upgraded from vgn6i)
All families run on the third-generation SHENLONG architecture with fast path acceleration, delivering storage, network, and compute stability improvements of an order of magnitude over traditional virtualization. Each family includes an NVIDIA GRID virtual workstation (vWS) license, providing certified graphics acceleration for Computer Aided Design (CAD) software and professional graphics applications.
Family comparison
| Family | GPU | CPU | CPU allocation | vGPU range | Storage |
|---|---|---|---|---|---|
| sgn8ia | NVIDIA Lovelace | AMD Genoa, 3.4–3.75 GHz | Shared (~1:1.5 overcommit) | 2 GB to 48 GB GPU memory | ESSDs, ESSD AutoPL disks |
| sgn7i-vws | NVIDIA A10 (Ampere) | Intel Xeon Ice Lake, 2.9/3.5 GHz | Shared | 1/12 to 1/3 of A10 (2–8 GB GPU memory) | ESSDs, ESSD AutoPL disks |
| vgn7i-vws | NVIDIA A10 (Ampere) | Intel Xeon Ice Lake, 2.9/3.5 GHz | Dedicated | 1/6 to full A10 (4–24 GB GPU memory) | ESSDs, ESSD AutoPL disks |
| vgn6i-vws | NVIDIA T4 | Intel Xeon Platinum 8163 (Skylake), 2.5 GHz | Dedicated | 1/4 to full T4 (4–16 GB GPU memory) | Standard SSDs, ultra disks |
How vGPU slicing works
Each physical GPU is sliced into multiple GPU partitions. Each partition is allocated as a vGPU to a single instance. The GPUs column in the instance type tables uses the format <GPU model> * <fraction> to show both the GPU model and the partition size allocated to each instance. For example, NVIDIA A10 * 1/6 means the instance receives one-sixth of an NVIDIA A10 GPU.
Use cases
| Use case | Description | Recommended families |
|---|---|---|
| Remote graphics and virtual workstations | Graphic design, CAD, animation, film production, mechanical design — accessed remotely with near-native GPU performance | sgn8ia, sgn7i-vws, vgn7i-vws |
| AI inference at scale | Concurrent inference for image recognition, speech recognition, and behavior identification | sgn8ia, sgn7i-vws, vgn7i-vws |
| Cloud gaming | Real-time GPU rendering for interactive cloud gaming and AR/VR applications | All families |
| 3D visualization | Professional-grade GPU rendering for graphics-intensive workloads | sgn8ia, sgn7i-vws, vgn7i-vws |
| Deep learning environments | Educational and experimental deep learning environments requiring GPU acceleration | vgn6i-vws |
sgn8ia
sgn8ia instances use NVIDIA Lovelace GPUs with large GPU memory and multiple GPU slicing options, paired with AMD Genoa processors running at 3.4 GHz to 3.75 GHz. CPUs are shared resources with an overcommit ratio of approximately 1:1.5. Memory and GPU memory are exclusive to each instance. Available GPU memory ranges from 2 GB to 48 GB (full GPU).
For workloads that require dedicated CPUs, use gn7i GPU-accelerated compute-optimized instances instead.
GPU: NVIDIA Lovelace — supports vGPU, RTX, and TensorRT
CPU: AMD Genoa, 3.4 GHz to 3.75 GHz (shared, ~1:1.5 overcommit)
Storage: I/O optimized; supports Enterprise SSDs (ESSDs) and ESSD AutoPL disks
Network: Supports IPv4 and IPv6
Use cases:
Concurrent AI inference — image recognition, speech recognition, behavior identification
Compute-intensive graphics processing — remote graphic design, cloud gaming
3D modeling — animation, film production, cloud gaming, mechanical design
Instance types
| Instance type | vCPUs | Memory (GiB) | GPU memory | Network baseline bandwidth (Gbit/s) | Packet forwarding rate (pps) | NIC queues | ENIs | Private IPv4/IPv6 addresses per ENI | Maximum disks | Disk baseline IOPS | Disk baseline BPS (MB/s) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ecs.sgn8ia-m2.xlarge | 4 | 16 | 2 GB | 2.5 | 1,000,000 | 4 | 4 | 15/15 | 9 | 30,000 | 244 |
| ecs.sgn8ia-m4.2xlarge | 8 | 32 | 4 GB | 4 | 1,600,000 | 8 | 4 | 15/15 | 9 | 45,000 | 305 |
| ecs.sgn8ia-m8.4xlarge | 16 | 64 | 8 GB | 7 | 2,000,000 | 16 | 8 | 30/30 | 17 | 60,000 | 427 |
| ecs.sgn8ia-m16.8xlarge | 32 | 128 | 16 GB | 10 | 3,000,000 | 32 | 8 | 30/30 | 33 | 80,000 | 610 |
| ecs.sgn8ia-m24.12xlarge | 48 | 192 | 24 GB | 16 | 4,500,000 | 48 | 8 | 30/30 | 33 | 120,000 | 1,000 |
| ecs.sgn8ia-m48.24xlarge | 96 | 384 | 48 GB | 32 | 9,000,000 | 64 | 15 | 30/30 | 33 | 24,000 | 2,000 |
GPU memory values represent vGPU memory allocated using vGPU slicing technology. CPUs are shared with an overcommit ratio of approximately 1:1.5. Memory and GPU memory are exclusive to each instance.
sgn7i-vws
sgn7i-vws instances use NVIDIA A10 GPUs (NVIDIA Ampere architecture) paired with Intel Xeon Scalable processors (Ice Lake, 2.9 GHz base, 3.5 GHz all-core turbo). CPU and network resources are shared to maximize utilization. Memory and GPU memory are exclusive to each instance. Available GPU memory ranges from 1/12 of an A10 GPU (2 GB) to 1/3 of an A10 GPU (8 GB).
For workloads that require dedicated CPUs, use vgn7i-vws instead.
GPU: NVIDIA A10 (Ampere architecture) — supports vGPU, RTX, and TensorRT
CPU: Intel Xeon Scalable (Ice Lake), 2.9 GHz base / 3.5 GHz all-core turbo (shared)
Storage: I/O optimized; supports ESSDs and ESSD AutoPL disks
Network: Supports IPv4 and IPv6 (shared)
Use cases:
Concurrent AI inference — image recognition, speech recognition, behavior identification
Compute-intensive graphics processing — remote graphic design, cloud gaming
3D modeling — animation, film production, cloud gaming, mechanical design
Instance types
| Instance type | vCPUs | Memory (GiB) | GPUs | GPU memory | Network baseline/burst bandwidth (Gbit/s) | Packet forwarding rate (pps) | NIC queues | ENIs | Private IPv4 addresses per ENI | IPv6 addresses per ENI |
|---|---|---|---|---|---|---|---|---|---|---|
| ecs.sgn7i-vws-m2.xlarge | 4 | 15.5 | NVIDIA A10 * 1/12 | 24GB * 1/12 | 1.5/5 | 500,000 | 4 | 2 | 2 | 1 |
| ecs.sgn7i-vws-m4.2xlarge | 8 | 31 | NVIDIA A10 * 1/6 | 24GB * 1/6 | 2.5/10 | 1,000,000 | 4 | 4 | 6 | 1 |
| ecs.sgn7i-vws-m8.4xlarge | 16 | 62 | NVIDIA A10 * 1/3 | 24GB * 1/3 | 5/20 | 2,000,000 | 8 | 4 | 10 | 1 |
| ecs.sgn7i-vws-m2s.xlarge | 4 | 8 | NVIDIA A10 * 1/12 | 24GB * 1/12 | 1.5/5 | 500,000 | 4 | 2 | 2 | 1 |
| ecs.sgn7i-vws-m4s.2xlarge | 8 | 16 | NVIDIA A10 * 1/6 | 24GB * 1/6 | 2.5/10 | 1,000,000 | 4 | 4 | 6 | 1 |
| ecs.sgn7i-vws-m8s.4xlarge | 16 | 32 | NVIDIA A10 * 1/3 | 24GB * 1/3 | 5/20 | 2,000,000 | 8 | 4 | 10 | 1 |
The GPUs column shows the GPU model and partition allocated per instance. NVIDIA A10 * 1/12 means each instance receives one-twelfth of an NVIDIA A10 GPU as a vGPU. CPU and network resources are shared; memory and GPU memory are exclusive to each instance.vgn7i-vws
vgn7i-vws instances use NVIDIA A10 GPUs (NVIDIA Ampere architecture) paired with Intel Xeon Scalable processors (Ice Lake, 2.9 GHz base, 3.5 GHz all-core turbo). Unlike sgn7i-vws, CPU resources are dedicated to each instance. Available GPU memory ranges from 1/6 of an A10 GPU (4 GB) to a full A10 GPU (24 GB).
GPU: NVIDIA A10 (Ampere architecture) — supports vGPU, RTX, and TensorRT
CPU: Intel Xeon Scalable (Ice Lake), 2.9 GHz base / 3.5 GHz all-core turbo (dedicated)
Storage: I/O optimized; supports ESSDs and ESSD AutoPL disks
Network: Supports IPv4 and IPv6
Use cases:
Concurrent AI inference — image recognition, speech recognition, behavior identification
Compute-intensive graphics processing — remote graphic design, cloud gaming
3D modeling — animation, film production, cloud gaming, mechanical design
Instance types
| Instance type | vCPUs | Memory (GiB) | GPUs | GPU memory | Network baseline bandwidth (Gbit/s) | Packet forwarding rate (pps) | NIC queues | ENIs | Private IPv4 addresses per ENI | IPv6 addresses per ENI |
|---|---|---|---|---|---|---|---|---|---|---|
| ecs.vgn7i-vws-m4.xlarge | 4 | 30 | NVIDIA A10 * 1/6 | 24GB * 1/6 | 3 | 1,000,000 | 4 | 4 | 10 | 1 |
| ecs.vgn7i-vws-m8.2xlarge | 10 | 62 | NVIDIA A10 * 1/3 | 24GB * 1/3 | 5 | 2,000,000 | 8 | 6 | 10 | 1 |
| ecs.vgn7i-vws-m12.3xlarge | 14 | 93 | NVIDIA A10 * 1/2 | 24GB * 1/2 | 8 | 3,000,000 | 8 | 6 | 15 | 1 |
| ecs.vgn7i-vws-m24.7xlarge | 30 | 186 | NVIDIA A10 * 1 | 24GB * 1 | 16 | 6,000,000 | 12 | 8 | 30 | 1 |
The GPUs column shows the GPU model and partition allocated per instance. NVIDIA A10 * 1/6 means each instance receives one-sixth of an NVIDIA A10 GPU as a vGPU. CPU resources are dedicated; memory and GPU memory are exclusive to each instance.vgn6i-vws
vgn6i-vws is the upgraded version of vgn6i, updated to use the latest NVIDIA GRID driver with an NVIDIA GRID vWS license.
Free images with the driver pre-installed: Submit a ticket to request a pre-installed image.
Custom images without the driver: Submit a ticket to apply for the driver file. Alibaba Cloud does not charge additional license fees.
vgn6i-vws instances use NVIDIA T4 GPUs paired with Intel Xeon Platinum 8163 processors (Skylake, 2.5 GHz). The CPU-to-memory ratio is 1:5. Supports 1/4 and 1/2 compute capacity of NVIDIA Tesla T4 GPUs, with 4 GB and 8 GB of GPU memory per vGPU instance.
GPU: NVIDIA T4 — supports 1/4 and 1/2 compute capacity, 4 GB and 8 GB GPU memory per vGPU instance
CPU: Intel Xeon Platinum 8163 (Skylake), 2.5 GHz
Storage: I/O optimized; supports standard SSDs and ultra disks
Network: Supports IPv4 and IPv6
Use cases:
Real-time rendering for cloud gaming
Real-time rendering for Augmented Reality (AR) and Virtual Reality (VR) applications
AI inference — deep learning and machine learning for elastic internet service deployment
Deep learning educational and experimental environments
Instance types
| Instance type | vCPUs | Memory (GiB) | GPUs | GPU memory | Network baseline bandwidth (Gbit/s) | Packet forwarding rate (pps) | NIC queues | ENIs | Private IPv4 addresses per ENI | IPv6 addresses per ENI |
|---|---|---|---|---|---|---|---|---|---|---|
| ecs.vgn6i-m4-vws.xlarge | 4 | 23 | NVIDIA T4 * 1/4 | 16GB * 1/4 | 2 | 500,000 | 4/2 | 3 | 10 | 1 |
| ecs.vgn6i-m8-vws.2xlarge | 10 | 46 | NVIDIA T4 * 1/2 | 16GB * 1/2 | 4 | 800,000 | 8/2 | 4 | 10 | 1 |
| ecs.vgn6i-m16-vws.5xlarge | 20 | 92 | NVIDIA T4 * 1 | 16GB * 1 | 7.5 | 1,200,000 | 6 | 4 | 10 | 1 |
The GPUs column shows the GPU model and partition allocated per instance. NVIDIA T4 * 1/4 means each instance receives one-quarter of an NVIDIA T4 GPU as a vGPU.