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Verge.io Unveils Shared, Virtualized GPU Computing to Cut Complexity and Cost

Retrieved on: 
Tuesday, August 16, 2022
Internet, Data Management, Apps, Applications, Technology, Software, Machine learning, Intel, NIST, Risk, Graphics processing unit, Public sector, Tesla, HPC, DevOps, NVIDIA, Nvidia Tesla, GPU, PCI, SOX, VDI, HIPAA, X86, CUI, University, Organization, Public, XSP, Cryptocurrency, Video game console, Network equipment

Current methods for deploying GPUs systemwide are complex and expensive, especially for remote users.

Key Points: 
  • Current methods for deploying GPUs systemwide are complex and expensive, especially for remote users.
  • Rather than supplying GPUs throughout the organization, Verge.io allows users and applications with access to a virtual data center to share the computing resources of a single GPU-equipped server.
  • Users/administrators can pass through an installed GPU to a virtual data center by simply creating a virtual machine with access to that GPU and its resources.
  • Alternatively, Verge.io can manage the virtualization of the GPU and serve up vGPUs to virtual data centers.

Data Center Accelerator Market worth $65.3 billion by 2026 - Exclusive Report by MarketsandMarkets™

Retrieved on: 
Thursday, July 15, 2021
Computing, Concurrent computing, Technology, Coprocessors, Data management, Technology forecasting, Nvidia, Artificial intelligence, Big data, AI accelerator

The key factors contributing to the growth of the data center accelerator market include growth of cloud-based services, focus on parallel computing in AI data center and deep learning usage in big data analytics, However, The burgeoning application of AI in various sectors has enhanced consumer perception and expectations from AI technologies.

Key Points: 
  • The key factors contributing to the growth of the data center accelerator market include growth of cloud-based services, focus on parallel computing in AI data center and deep learning usage in big data analytics, However, The burgeoning application of AI in various sectors has enhanced consumer perception and expectations from AI technologies.
  • Various AI technologies built to date have failed to make a larger impact in the AI market.
  • For instance, owing to the high cost of data center accelerators such as NVIDIA Tesla products, a majority of data center manufacturers are reluctant about adopting those accelerators in their products.
  • With the exponential data growth, data center operators have to establish a balance between the need for the performance at scale and the operational efficiencies.

Data Center Accelerator Market worth $65.3 billion by 2026 - Exclusive Report by MarketsandMarkets™

Retrieved on: 
Thursday, July 15, 2021
Computing, Concurrent computing, Technology, Coprocessors, Data management, Technology forecasting, Nvidia, Artificial intelligence, Big data, AI accelerator

The key factors contributing to the growth of the data center accelerator market include growth of cloud-based services, focus on parallel computing in AI data center and deep learning usage in big data analytics, However, The burgeoning application of AI in various sectors has enhanced consumer perception and expectations from AI technologies.

Key Points: 
  • The key factors contributing to the growth of the data center accelerator market include growth of cloud-based services, focus on parallel computing in AI data center and deep learning usage in big data analytics, However, The burgeoning application of AI in various sectors has enhanced consumer perception and expectations from AI technologies.
  • Various AI technologies built to date have failed to make a larger impact in the AI market.
  • For instance, owing to the high cost of data center accelerators such as NVIDIA Tesla products, a majority of data center manufacturers are reluctant about adopting those accelerators in their products.
  • With the exponential data growth, data center operators have to establish a balance between the need for the performance at scale and the operational efficiencies.

Supermicro Introduces AI Inference-optimized New GPU Server with up to 20 NVIDIA Tesla T4 Accelerators in 4U

Retrieved on: 
Thursday, September 20, 2018
GPGPU, Computer architecture, Parallel computing, Video cards, Computing, Coprocessors, Computer engineering, Graphics hardware, Graphics processing unit, Nvidia Tesla, Nvidia, Tesla

For maximum GPU density and performance, this 4U server supports up to 20 NVIDIA Tesla T4 Tensor Core GPUs, three terabytes of memory, and 24 hot-swappable 3.5" drives.

Key Points: 
  • For maximum GPU density and performance, this 4U server supports up to 20 NVIDIA Tesla T4 Tensor Core GPUs, three terabytes of memory, and 24 hot-swappable 3.5" drives.
  • "With AI inference constituting an increasingly large portion of data center workloads, these Tesla T4 GPU platforms provide incredibly efficient real-time and batch inference."
  • Supermicro's performance-optimized 4U SuperServer 6049GP-TRT system can support up to 20 PCI-E NVIDIA Tesla T4 GPU accelerators, which dramatically increases the density of GPU server platforms for wide data center deployment supporting deep learning, inference applications.
  • Supermicro has an entire family of 4U GPU systems that support the ultra-efficient Tesla T4, which is designed to accelerate inference workloads in any scale-out server.

Supermicro Introduces AI Inference-optimized New GPU Server with up to 20 NVIDIA Tesla T4 Accelerators in 4U

Retrieved on: 
Thursday, September 20, 2018
GPGPU, Computer architecture, Parallel computing, Video cards, Computing, Coprocessors, Computer engineering, Graphics hardware, Graphics processing unit, Nvidia Tesla, Nvidia, Tesla

For maximum GPU density and performance, this 4U server supports up to 20 NVIDIA Tesla T4 Tensor Core GPUs, three terabytes of memory, and 24 hot-swappable 3.5" drives.

Key Points: 
  • For maximum GPU density and performance, this 4U server supports up to 20 NVIDIA Tesla T4 Tensor Core GPUs, three terabytes of memory, and 24 hot-swappable 3.5" drives.
  • "With AI inference constituting an increasingly large portion of data center workloads, these Tesla T4 GPU platforms provideincredibly efficient real-time and batch inference."
  • Supermicro's performance-optimized 4U SuperServer 6049GP-TRT system can support up to 20 PCI-E NVIDIA Tesla T4 GPU accelerators, which dramatically increases the density of GPU server platforms for wide data center deployment supporting deep learning, inference applications.
  • Supermicro has an entire family of 4U GPU systems that support the ultra-efficient Tesla T4, which is designed to accelerate inference workloads in any scale-out server.