VIRTUALIZATION SOFTWARE ON NVIDIA GRAPHICS PROCESSORS
NVIDIA GPUs use NVIDIA virtual GPU (vGPU) software.
see below to find the right GPU for your needs.
see below to find the right GPU for your needs.
VIRTUALIZATION SOFTWARE ON NVIDIA GRAPHICS PROCESSORS
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GPUs | A100 | A30 | A40 | A16 | A10 | A2 |
Graphics Processor Architecture | NVIDIA ampere | NVIDIA ampere | NVIDIA ampere | NVIDIA ampere | NVIDIA ampere | NVIDIA ampere |
Memory size | 80 GB HBM2e | 24 GB HMB2 | 48 GB GDDR6 with ECC | 64 GB GDDR6 (16 GB per GPU) | 24 GB GDDR6 | 16 GB GDDR6 |
Virtualization workload | Highest performance virtualized computing including AI, HPC, and data processing, including support for up to 7 MIG instances. Upgrade path for V100/V100S Tensor Core GPUs. | Virtualize mainstream computing and AI inference and support up to 4 MIG instances. | Develop advanced, high-quality 3D designs and creative workflows with NVIDIA RTX® Virtual Workstation (vDWS). Upgrade path for Quadro RTX™ 8000, RTX 6000. | Office productivity apps, streaming video and teleconferencing tools for graphics-rich virtual desktops you can access from anywhere. Upgrade path for M10. | Office productivity applications, streaming video and teleconferencing tools for graphics-rich virtual desktops that you can access from anywhere. | Entry-level inference with low power consumption. small footprint and high performance for intelligent video analytics (IVA) with NVIDIA AI in the Edge. 7x more inference performance |
vGPU software support | NVIDIA Virtual Compute Server (vCS) | NVIDIA vCS | NVIDIA RTX vWS, NVIDIA Virtual PC (vPC), NVIDIA Virtual Apps (vApps), vCS | NVIDIA RTX vWS, vPC, vApps, vCS | vPC/vApps, vCS, vWs | vPC/vApps, vCS, vWs, NVIDIA AI Enterprise |
Data sheets | A100 | A30 | A40 | A16 | A10 | A2 |
About the product | More info | More info | More info | More info | More info | More info |
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GPU | A6000 | A5500 | A5000 | A4500 | A4000 | A2000 |
Graphics Processor Architecture | NVIDIA ampere | NVIDIA ampere | NVIDIA ampere | NVIDIA ampere | NVIDIA ampere | NVIDIA ampere |
Memory size | 48 GB GDDR6 | 24 GB GDDR6 | 24 GB GDDR6 | 20 GB GDDR6 | 16 GB GDDR6 | 6 GB GDDR6 or 12 GB GDDR6 |
Virtualization workload | Ultra-high-performance rendering, simulation, and 3D design with NVIDIA vWS. AI, deep learning, and data science with NVIDIA vCS. | Turn your personal workstation into multiple virtual workstations with support for NVIDIA RTX Virtual Workstation (vWS) software. | Work with the largest and most complex RTX-enabled rendering, 3D design, and creative applications with NVIDIA vWS. | Work with the largest and most complex RTX-enabled rendering, 3D design, and creative applications with NVIDIA vWS. | Use RTX applications and NVIDIA vWS to develop advanced, high-quality 3D designs and creative workflows. | Small and power-efficient graphics card for compact workstations, including Tensor compute cores for ray tracing and AI accelerations. |
vGPU software support | vWS, vPC, vApp, vCS | vWS, vPC, vApp | vWS, vPC, vApp, vCS | vWS, vPC, vApp, vCS | vWS, vPC, vApp, vCS | vWS, vPC, vApps, vCS |
Data sheet | A6000 | A5500 | A5000 | A4500 | A4000 | A2000 |
About the product | More info | More info | More info | More info | More info | More info |
ACCELERATED COMPUTING PERFORMANCE, VIRTUALIZED
AI, deep learning, and data science require unprecedented computing power. NVIDIA's Virtual Compute Server (vCS) enables data centers to accelerate server virtualization with the latest NVIDIA data center GPUs, such as the NVIDIA A100 and A30 Tensor Core GPUs, so that even the most compute-intensive workloads, such as.such as artificial intelligence, deep learning, and data science, can run on a virtual machine (VM) with NVIDIA vGPU technology. This is not a small step for virtualization, but a big leap.
vCS SOLUTION OVERVIEW
SCALED FOR MAXIMUM EFFICIENCY
NVIDIA Virtual GPUs give you near bare-metal performance in a virtualized environment, maximum utilization, management, and monitoring in a hypervisor-based virtualization environment for GPU-accelerated AI.
Performance scaling for deep learning training with vCS on NVIDIA A100 tensor core GPUs
Developers, data scientists, researchers, and students need massive computing power for deep learning training. Our A100 Tensor Core GPU accelerates the work, allowing more to be accomplished faster. NVIDIA software, Virtual Compute Server, provides nearly the same performance as bare metal, even when scaled to large deep learning training models that use multiple GPUs.
Throughput performance for deep learning inference with MIG on NVIDIA A100 tensor core GPUs with vCS
Multi-Instance GPU (MIG) is a technology found only on the NVIDIA A100 tensor core GPU that partitions the A100 GPU into up to seven instances, each fully isolated with its own high-bandwidth memory and cache and cores. MIG can be used with Virtual Compute Server, providing one VM per MIG instance, and performance is consistent whether running an inference workload across multiple MIG instances on bare metal or virtualized with vCS.
RESOURCES FOR IT MANAGERS
Learn how NVIDIA Virtual Compute Server maximizes performance and simplifies IT management.
Utilization optimization
Take full advantage of valuable GPU resources by seamlessly splitting GPUs for simpler workloads like inference or deploying multiple virtual GPUs for more compute-intensive workloads like deep learning training.
Manageability and monitoring
Ensure the availability and readiness of the systems that data scientists and researchers need. Monitor GPU performance at the guest, host, and application levels. You can even leverage management tools like suspend/resume and live migration. Learn more about the operational benefits of GPU virtualization.
DISCOVER ADVANTAGES FOR IT