BERT Large Inference | NVIDIA T4 Tensor Core GPU: NVIDIA TensorRT™ (TRT) 7.1, precision = INT8, batch size = 256 | V100: TRT 7.1, precision = FP16, batch size = 256 | A100 with 7 MIG instances of 1g.5gb: pre-production TRT, batch size = 94, precision = INT8 with sparsity.Highest versatility for all workloads.Learn what’s new with the NVIDIA Ampere architecture and its implementation in the NVIDIA A100 GPU.Geometric mean of application speedups vs. P100: benchmark application: Amber [PME-Cellulose_NVE], Chroma [szscl21_24_128], GROMACS [ADH Dodec], MILC [Apex Medium], NAMD [stmv_nve_cuda], PyTorch (BERT Large Fine Tuner], Quantum Espresso [AUSURF112-jR]; Random Forest FP32 [make_blobs (160000 x 64 : 10)], TensorFlow [ResNet-50], VASP 6 [Si Huge], | GPU node with dual-socket CPUs with 4x NVIDIA P100, V100, or A100 GPUs.BERT Large Inference | NVIDIA TensorRT™ (TRT) 7.1 | NVIDIA T4 Tensor Core GPU: TRT 7.1, precision = INT8, batch size = 256 | V100: TRT 7.1, precision = FP16, batch size = 256 | A100 with 1 or 7 MIG instances of 1g.5gb: batch size = 94, precision = INT8 with sparsity.Customers need to be able to analyze, visualize, and turn massive datasets into insights. Experts will also help you plan profiles for the new Multi Instance GPU features.A Microway Solutions Architect will provide remote consultation to you in planning for the DGX A100’s unique power and cooling requirements. Jim Salter - May 15, 2020 10:45 am UTC. Despite coming in at a starting price of $199,000, Nvidia stated that the performance of this supercomputer makes the DGX A100 an affordable solution. “The Nvidia DGX A100 delivers a tremendous leap in performance and capabilities,” explained Nvidia’s Vice President and General Manager of DGX …
Release date. NVIDIA DGX A100 is the new standard for AI infrastructure and features dramatic upgrades to performance for any workload. These benchmarks measure training times for a maximum scaled system (like the NVIDIA Selene, which is the world’s 7th fastest Supercomputer) and for a single server (such as the 8GPU DGX A100). Training them requires massive compute power and scalability.BERT pre-training throughput using Pytorch, including (2/3) Phase 1 and (1/3) Phase 2 | Phase 1 Seq Len = 128, Phase 2 Seq Len = 512; V100: NVIDIA DGX-1™ server with 8x V100 using FP32 precision; A100: DGX A100 Server with 8x A100 using TF32 precision.Unprecedented Acceleration at Every ScaleTo unlock next-generation discoveries, scientists look to simulations to better understand complex molecules for drug discovery, physics for potential new sources of energy, and atmospheric data to better predict and prepare for extreme weather patterns.Ultimate performance for all workloads. This DGX system includes 8 NVIDIA A100 GPUs interconnected with NVIDIA NVLink technology.Continuing to renew your Support services ensures you receive the latest DGX software updates (including frameworks) and retain the DGX A100 system’s outstanding SLA commitments.DGX A100 deliveries are bundled with Microway services including:All DGX OS and container software will be installed, firmware upgraded to the latest versions, desired DGX-containers installed, and deep learning test jobs run.
But scale-out solutions often become bogged down as these datasets are scattered across multiple servers.AI models are exploding in complexity as they take on next-level challenges such as accurate conversational AI and deep recommender systems. The A100 Tensor Core GPU demonstrated the fastest performance per accelerator on all eight MLPerf benchmarks. The AMD CPUs we use have 64 cores each, lots of PCI lanes, and support PCIe Gen4,” added Boyle.“Only 2nd Gen AMD EPYC processors can provide up to 64 cores and 128 lanes of PCIe 4 interconnectivity in a single x86 data center processor, and we’re excited to see how the power of the Nvidia DGX A100 system enables the I/O bandwidth to be effectively doubled,” said Raghu Nambiar, AMD’s corporate vice president of data center ecosystems and application engineering.“The Nvidia DGX A100 delivers a tremendous leap in performance and capabilities,” explained Nvidia’s Vice President and General Manager of DGX Systems, Charlie Boyle. This means that the DGX solution will utilize 1/20th the power and occupy 1/25th the space of a traditional server solution at 1/10th the cost.While the DGX A100 can be purchased starting today, some institutions — like the University of Florida, which uses the computer to create an A.I.-focused curriculum, and others — have already been using the supercomputer to accelerate A.I.-powered solutions and services ranging from healthcare to understanding space and energy consumption.Each GPU instance gets its own dedicated resources — like the memory, cores, memory bandwidth, and cache.
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