The Most Powerful End-to-End AI and HPC Data Center Platform
NVIDIA A100 Tensor Core GPU delivers unprecedented acceleration at every scale to power the world’s highest-performing elastic data centers for AI, data analytics, and HPC. Powered by the NVIDIA Ampere Architecture, A100 is the engine of the NVIDIA data center platform. A100 provides up to 20X higher performance over the prior generation and can be partitioned into seven GPU instances to dynamically adjust to shifting demands. Available in 40GB and 80GB memory versions, A100 80GB debuts the world’s fastest memory bandwidth at over 2 terabytes per second (TB/s) to run the largest models and datasets.
Unprecedented Acceleration for World’s Highest-Performing Elastic Data Centers
The NVIDIA A100 Tensor Core GPU is the flagship product of the NVIDIA data center platform for deep learning, HPC, and data analytics. The platform accelerates over 2,000 applications, including every major deep learning framework. A100 is available everywhere, from desktops to servers to cloud services, delivering both dramatic performance gains and cost-saving opportunities.
|A100 40GB PCIe||A100 80GB PCIe||A100 40GB SXM||A100 80GB SXM|
|FP64 Tensor Core||19.5 TFLOPS|
|Tensor Float 32 (TF32)||156 TFLOPS | 312 TFLOPS*|
|BFLOAT16 Tensor Core||312 TFLOPS | 624 TFLOPS*|
|FP16 Tensor Core||312 TFLOPS | 624 TFLOPS*|
|INT8 Tensor Core||624 TOPS | 1248 TOPS*|
|GPU Memory||40GB HBM2||80GB HBM2e||40GB HBM2||80GB HBM2e|
|GPU Memory Bandwidth||1,555GB/s||1,935GB/s||1,555GB/s||2,039GB/s|
|Max Thermal Design Power (TDP)||250W||300W||400W||400W|
|Multi-Instance GPU||Up to 7 MIGs @ 5GB||Up to 7 MIGs @ 10GB||Up to 7 MIGs @ 5GB||Up to 7 MIGs @ 10GB|
|Interconnect||NVIDIA® NVLink® Bridge for 2 GPUs: 600GB/s **
PCIe Gen4: 64GB/s
PCIe Gen4: 64GB/s
|Server Options||Partner and NVIDIA-Certified Systems™ with 1-8 GPUs||NVIDIA HGX™ A100-Partner and NVIDIA-Certified Systems with 4,8, or 16 GPUs NVIDIA DGX™ A100 with 8 GPUs|
NVIDIA AMPERE ARCHITECTURE
Whether using MIG to partition an A100 GPU into smaller instances or NVLink to connect multiple GPUs to speed large-scale workloads, A100 can readily handle different-sized acceleration needs, from the smallest job to the biggest multi-node workload. A100’s versatility means IT managers can maximize the utility of every GPU in their data center, around the clock.
THIRD-GENERATION TENSOR CORES
NVIDIA A100 delivers 312 teraFLOPS (TFLOPS) of deep learning performance. That’s 20X the Tensor floating-point operations per second (FLOPS) for deep learning training and 20X the Tensor tera operations per second (TOPS) for deep learning inference compared to NVIDIA Volta GPUs.
NVIDIA NVLink in A100 delivers 2X higher throughput compared to the previous generation. When combined with NVIDIA NVSwitch™, up to 16 A100 GPUs can be interconnected at up to 600 gigabytes per second (GB/sec), unleashing the highest application performance possible on a single server. NVLink is available in A100 SXM GPUs via HGX A100 server boards and in PCIe GPUs via an NVLink Bridge for up to 2 GPUs.
MULTI-INSTANCE GPU (MIG)
An A100 GPU can be partitioned into as many as seven GPU instances, fully isolated at the hardware level with their own high-bandwidth memory, cache, and compute cores. MIG gives developers access to breakthrough acceleration for all their applications, and IT administrators can offer right-sized GPU acceleration for every job, optimizing utilization and expanding access to every user and application.
HIGH-BANDWIDTH MEMORY (HBM2E)
With up to 80 gigabytes of HBM2e, A100 delivers the world’s fastest GPU memory bandwidth of over 2TB/s, as well as a dynamic randomaccess memory (DRAM) utilization efficiency of 95%. A100 delivers 1.7X higher memory bandwidth over the previous generation.
AI networks have millions to billions of parameters. Not all of these parameters are needed for accurate predictions, and some can be converted to zeros, making the models “sparse” without compromising accuracy. Tensor Cores in A100 can provide up to 2X higher performance for sparse models. While the sparsity feature more readily benefits AI inference, it can also improve the performance of model training.
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