The Tesla A2 GPU server is designed for users who need efficient AI inference with low power consumption. It is ideal for serving AI models, edge computing, and scalable inference workloads, where performance per watt is more important than raw training power.
Built for modern data center environments, the A2 handles multi-instance GPU workloads and virtualized environments (vGPU), making it a practical choice for deploying AI services, automation systems, and distributed applications.
Nvidia Tesla A2 Specs:
| Processors | |
| Cuda Core | 1280 |
| Tensor Core | 40 (3rd Gen) |
| Ray Tracing Core | 10 (2nd Gen) | Memory |
| Size | 16GB |
| Type | GDDR6 ECC |
| Bus | 128 bit |
| bandwidth | 200.1 GB/s |
Nvidia Tesla A2 Benchmarks
The Tesla A2 delivers efficient inference-focused performance, optimized for scalable AI workloads and virtualized GPU environments.
G3D Mark
Overall Performance
3,331
Points
OctaneBench
Rendering Performance
120
Points
FP32
Compute Performance (Cuda Core)
4,500
GFLOPS
TF32
A.I. Performance (Tensor Core)
9,000
GFLOPS
GPU Benchmark Comparison Table
Compare GPUs by benchmark scores and sort any column.
| GPU |
G3DMark
GPU performance score from PassMark Software's G3D Mark, widely used for overall GPU performance comparison. Higher is better. References: PassMark (Points) |
OctaneBench
Rendering benchmark score from OTOY's OctaneBench 2020, widely used for GPU rendering performance comparison. Higher is better. References: OctaneBench (Points) |
FP32
Theoretical FP32 (single-precision) performance of GPUs based on NVIDIA CUDA cores and AMD Stream processors, widely used for compute performance comparison. Higher is better. References: nVidia, AMD, TechPowerUp (GFLOPS) |
TF32
Theoretical TF32 (Tensor Float 32) performance of GPUs based on NVIDIA Tensor Cores, widely used for AI workload performance comparison. Higher is better. References: nVidia, TechPowerUp (GFLOPS) |
vGPU Support (modes) |
|---|---|---|---|---|---|
Core Features of GPU Dedicated Server Hosting
Comparing a virtual machine with a dedicated server gpu
gives a clear overview of performance, control and hardware isolation benefits.
Virtualization Technology
VT allows the server's operating system to run guest environments (like virtual machines or emulators). Unlike VPS, VT is enabled on dedicated servers. Also, known as "Intel-HV" and AMD-V".
Dedicated Performance
Have the access to the underlying physical hardware where your operating system will be single-tenant, with no virtualization layer; hence, it is 100% all yours. You are inviolable from shared resources, and CPU/IOPS limits.
Private Network
Connect your servers directly to each other without the Internet. You can create VLAN between dedicated servers through an Internal/private port independently along the internet/public port.
IPMI/KVM
To manage server power, hardware health, and install the operating system, a web-based connection to the server is necessary even when server is not accessible from the internet.
GPU Server Use Case
Discover how our gpu server dedicated solutions empower developers, businesses
and marketers to achieve more with speed, security and full flexibility.
A.I. Dedicated Servers
Designed for training, fine tuning and running A.I. models using dedicated server with gpu acceleration and stable compute power.
Buy A.I. Servers now!
Android Emulator
Perfect for running multiple Android instances, automation tasks and mobile app testing on a dedicated server with graphics card performance.
Buy Android Emulator Servers now!
Gaming Server
Optimized for smooth gameplay, low latency and private hosting using gpu dedicated server hosting instead of shared resources.
Data Analytics & Processing
Suitable for large datasets, real time analytics and complex computations that benefit from dedicated gpu server hosting and parallel GPU processing.
3D Rendering & Animation
High performance dedicated server gpu power for rendering complex 3D scenes, animations and high resolution frames with reduced render time.
VFX & CGI Production
Optimized gpu dedicated server hosting for studios handling simulations, compositing and advanced CGI workflows.
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How To Pay Risk Free
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Frequently Asked Questions
Here we cover abroad range of frequently asked questions which
answers new audiences questions
What can I use a GPU Server for?
GPU servers are ideal for AI model training, LLM fine tuning, deep learning, machine learning pipelines, 3D rendering, simulations, video processing, and GPU intensive scientific workloads.
Is GPU passthrough supported for Docker?
Yes. You can enable NVIDIA Container Toolkit to pass GPU resources directly into Docker containers for AI and compute workloads.
Are GPU servers suitable for generative AI?
Yes. GPU servers are fully optimized for AI, text-to-image, image-to-video, LoRA training, and other generative AI tasks.
Can I use the server for video rendering or CGI?
Yes. GPU servers deliver high throughput for rendering in Blender, Unreal Engine, Redshift, Octane, and other 3D/CGI tools.
Can I upgrade or change GPUs later?
Yes. You can request GPU upgrades, add more GPUs, or switch GPU models depending on availability.
Are GPU servers suitable for inference and production APIs?
Yes. You can deploy inference endpoints, vector search engines, embeddings services, and real-time AI APIs.
Do GPU servers support virtualization?
Yes. You can run hypervisors like Proxmox, VMware ESXi, or KVM to create isolated VMs with GPU passthrough depending on your hardware configuration.
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Hardware issues are fixed within 3 hours, we guarantee 99.99% uptime. For each hour of downtime, OperaVPS provides a 5% credit of the server's monthly cost.