NVIDIA Tesla GPU Computing

NVIDIA® Tesla® GPUs deliver supercomputing performance at a lower power, lower cost, and using many fewer servers than standard CPU-only compute systems.

NVIDIA Elite Solution ProviderPowering the world’s leading Supercomputers, Microway designs customized GPU clusters, servers, and WhisperStations based on NVIDIA Tesla and NVIDIA Quadro® GPUs. We have been selected as the vendor of choice for a number of NVIDIA GPU Research Centers, including Carnegie Mellon University, Harvard, Johns Hopkins and Massachusetts General Hospital.

NVIDIA A100 GPU

NVIDIA A100 – A Massive Leap for Every Workload

Integrated in Microway Navion and NumberSmasher GPU Servers

Specifications

  • Up to 9.7 TFLOPS double- and 19.5 TFLOPS single-precision floating-point performance
  • Up to 19.5 TFLOPS Tensor Core double- and 156 TFLOPS Tensor Core single-precision floating-point performance
  • Up to 312 Tensor TFLOPS of Deep Learning Performance (FP16 Tensor Core)
  • NVIDIA Ampere™ GPU architecture
  • 3rd Generation NVIDIA NVLink interface, with 600GB/sec bi-directional bandwidth to the GPU (12 NVLink bricks)
  • 6912 FP32 CUDA cores, 432 Ampere Tensor Cores
  • 40GB on-die HBM2 GPU memory
  • Memory bandwidth up to 1,555 GB/s
  • NVIDIA NVLink™ GPU:GPU interface and PCI-E Gen4 link to system
  • Available in double-width PCI-E card and proprietary SXM4 form factors
  • Passive heatsink only, suitable for specially-designed GPU servers

Tesla V100 GPU

Tesla V100 – Advanced Datacenter GPU, for AI & HPC

Integrated in Microway NumberSmasher and OpenPOWER GPU Servers & GPU Clusters

SpecificationsTesla V100 SXM 2.0 GPU

  • Up to 7.8 TFLOPS double- and 15.7 TFLOPS single-precision floating-point performance
  • Up to 125 Tensor TFLOPS of Deep Learning Performance (FP16 Tensor Core)
  • NVIDIA Volta™ GPU architecture
  • 5120 CUDA cores, 620 Tensor Cores
  • 16GB or 32GB of on-die HBM2 GPU memory
  • Memory bandwidth up to 900GB/s
  • NVIDIA NVLink™ or PCI-E x16 Gen3 interface to system
  • Available with enhanced NVLink interface, with 300GB/sec bi-directional bandwidth to the GPU
  • Passive heatsink only, suitable for specially-designed GPU servers

Tesla T4 GPU

Tesla T4 – Price/performance for AI and Single Precision

Integrated in Microway NumberSmasher and Navion GPU Servers & GPU Clusters

Specifications

  • Up to 8.1 TFLOPS single-precision floating-point performance
  • Up to 65 TensorTFLOPS of Deep Learning Training Performance; 260 INT4 TOPS of Inference Performance
  • NVIDIA “Turing” TU104 graphics processing unit (GPU)
  • 2560 CUDA cores, 320 Tensor Cores
  • 16GB of GDDR6 GPU memory
  • Memory bandwidth up to 320GB/s
  • PCI-E x16 Gen3 interface to system
  • Passive heatsink only, suitable for specially-designed GPU servers

Unique features available in the latest NVIDIA GPUs include:

  • NVIDIA GK110 DieHigh-speed, on-die GPU memory
  • NVLink interconnect speeds up data transfers up to 10X over PCI-Express
  • Unified Memory allows applications to directly access the memory of all GPUs and all of system memory
  • Direct CPU-to-GPU NVLink connectivity on OpenPOWER systems support NVLink transfers between the CPUs and GPUs
  • ECC memory error protection – meets a critical requirement for computing accuracy and reliability in data centers and supercomputing centers.
  • System monitoring features – integrate the GPU subsystem with the host system’s monitoring and management capabilities such as IPMI. IT staff can manage the GPU processors in the computing system with widely-used cluster/grid management tools.

Many of the most popular applications already feature GPU support. Your own applications may take advantage of GPU acceleration through several different avenues:

  • “Drop-in” GPU-accelerated libraries – provide high-speed implementations of the functions your application currently executes on CPUs.
  • OpenACC / OpenMP Compiler directives – allow you to quickly add GPU acceleration to the most performance critical sections of your application while maintaining portability.
  • CUDA integrated with C, C++ or Fortran – provides maximum performance and flexibility for your applications. Third-party language extensions are available for a host of languages, including Java, Mathematica, MATLAB, Perl and Python.

Tesla GPU computing solutions fit seamlessly into your existing workstation or HPC infrastructure enabling you to solve problems orders-of-magnitude faster.

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