Supply, Installation & Commissioning Of Artificial Intelligence Lab Solution For The Use Of Computer Science Department Of The University , Gpu Accelerated Ai Computing And Developer Workstation Processor:Single Amd Epyc 7742 64 Core, 2.25Ghz Cpu Or Be, M G University-Kerala
TDR : 30343401
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M G University
Kerala
Corrigendum : Supply, Installation & Commissioning Of Artificial Intelligence Lab Solution For The Use Of Computer Science Department Of The University , Gpu Accelerated Ai Computing And Developer Workstation Processor:Single Amd Epyc 7742 64 Core, 2.25Ghz Cpu Or Be
Name of Work: Supply, Installation & Commissioning of Artificial Intelligence Lab Solution for the use of Computer Science Department of the University
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Supply, Installation & Commissioning of Artificial Intelligence Lab Solution for the use of Computer Science Department of the University
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GPU Accelerated AI Computing and Developer Workstation Processor:Single AMD EPYC 7742 64 core, 2.25GHz CPU or betterSystem Memory:512 GB DDR4GPU: 4 x NVIDIA A100 NVlink, 40GB Memory per GPU Performance:2.5 PetaFLOPS AI, 5 petaOPS INT8CUDA Cores:Minimum5000 per GPU Tensor Cores: Minimum400 per GPU Power Requirements:1.5KW or less Storage: 1X 1.92 TB NVMe Drive and Internal Storage1x7.68TB NVMe driveSystem Network: Dual 10 GbE, Single Port 1 GbEGPU communications protocol:NVLink, 600 gigabytes per second (GB/s) Bidirectional bandwidth OS Support: Ubuntu Linux USB Port:3 Noise level: < 40 db Display: 4X Mini Display port, 4GB GPU Memory Cooling:Workstation will be liquid cooled. No additional cooling support will be provided for the system. Software with Support (Directly from OEM with updates& upgrades): NVIDIA NGC with 3 years support, CUDA tuned Neural Network (cu DNN) Primitives Tensor RT Inference Engine Deep Stream SDK Video Analytics CUDA tuned BLAS CUDA tuned Sparse Matrix Operations (cu SPARSE) Multi-GPU Communications (NCCL), Kubernetes Tensor Flow, Caffe ,Py Torch, Theano,Keras, caffe2, CNTK software containers Warranty & Support: 3 Years, Standard Form factor:Workstation
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ML & DL BenchmarkingTime to Train a BERT model on WikipediaDataSet: Not more than 130 minutes.Time to Train a SSD model on COCODataSet: Not more than 30 minutes.HPL Benchmarking: FP64 45TF HPL Sustained Performance.
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