Training them requires massive compute power and scalability.Learn what’s new with the NVIDIA Ampere architecture and its implementation in the NVIDIA A100 GPU.Customers need to be able to analyze, visualize, and turn massive datasets into insights. But scale-out solutions often become bogged down as these datasets are scattered across multiple servers.To 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.Highest versatility for all workloads.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.Unprecedented Acceleration at Every ScaleUltimate performance for all workloads.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.
The server is the first generation of the DGX series to use AMD CPUs. This is mainly due to the less time it takes for the card to achieve the said tasks however, in complex situations which required sustained GPU capabilities, the GPU can deliver anywhere from up to 90% to down to 50% the performance of the 400W GPU in the most extreme cases. 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.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.AI models are exploding in complexity as they take on next-level challenges such as accurate conversational AI and deep recommender systems. If the new Ampere architecture based A100 Tensor Core data center GPU is the component responsible re-architecting the data center, NVIDIA’s new DGX A100 AI supercomputer is the ideal enabler to revitalize data centers. The DGX A100 Server: 8x A100s, 2x AMD EPYC CPUs, and PCIe Gen 4. NVIDIA told that the 50% drop will be very rare and only a few tasks can push the card to such extend.There's a wide scale adoption being made possible already by NVIDIA and its server partners for the said PCIe based GPU accelerator which include:Just like the Pascal P100 and Volta V100 before it, the Ampere A100 GPU was bound to get a PCIe variant sooner or later. The FP64 performance is still rated at 9.7/19.5 TFLOPs, FP32 performance is rated at 19.5 /156/312 TFLOPs (Sparsity), FP16 performance is rated at 312/624 TFLOPs (Sparsity) & INT8 is rated at 624/1248 TOPs (Sparsity).In terms of specifications, the A100 PCIe GPU accelerator doesn't change much in terms of core configuration. In addition to the Ampere architecture and A100 GPU that was announced, NVIDIA also announced the new DGX A100 server. The main difference can be seen in the TDP which is rated at 250W for the PCIe variant whereas the standard variant comes with a 400W TDP. NVIDIA's DGX A100 supercomputer is the ultimate instrument to advance AI and fight Covid-19. NVIDIA's A100 Ampere GPU Gets PCIe 4.0 Ready Form Factor - Same GPU Configuration But at 250W, Up To 90% Performance of the Full 400W A100 GPU. Now NVIDIA has announced that its A100 PCIe GPU accelerator is available for a diverse set of use cases with system ranging from a single A100 PCIe GPU to servers utilizing two cards at the same time through the 12 NVLINK channels that deliver 600 GB/s of interconnect bandwidth.For latest tech news in your inbox, once a day!Now we can guess that the card would feature lower clocks to compensate for the less TDP input but NVIDIA has provided the peak compute numbers and those remain unaffected for the PCIe variant. NVIDIA hasn't announced any release date or pricing for the card yet but considering the A100 (400W) Tensor Core GPU is already being shipped since its launch, the A100 (250W) PCIe will be following its footsteps soon.According to NVIDIA, the A100 PCIe accelerator can deliver 90% the performance of the A100 HGX card (400W) in top server applications. The GA100 GPU retains the specifications we got to see on the 400W variant with 6912 CUDA cores arranged in 108 SM units, 432 Tensor Cores and 40 GB of HBM2 memory that delivers the same memory bandwidth of 1.55 TB/s (rounded off to 1.6 TB/s).
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