NVIDIA A100 Tensor Core GPU

Unprecedented acceleration at every scale

Accelerating the Most Important Work of Our Time

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.

NVIDIA A100 Datasheet (PDF 612 KB)
A100 80GB PCIe Product Brief (PDF 380 KB)
A100 40GB PCIe Product Brief (PDF 332 KB)

Enterprise-Ready Software for AI

The NVIDIA EGX™ platform includes optimized software that delivers accelerated computing across the infrastructure. With NVIDIA AI Enterprise, businesses can access an end-to-end, cloud-native suite of  AI and data analytics software that’s  optimized, certified, and supported by NVIDIA to run on VMware vSphere  with  NVIDIA-Certified  Systems. NVIDIA AI Enterprise includes key enabling technologies  from NVIDIA for  rapid deployment, management, and scaling of AI workloads  in the modern hybrid cloud. 

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The Most Powerful End-to-End AI and HPC Data Center Platform

A100 is part of the complete NVIDIA data center solution that incorporates building blocks across hardware, networking, software, libraries, and optimized AI models and applications from NGC™. Representing the most powerful end-to-end AI and HPC platform for data centers, it allows researchers to deliver real-world results and deploy solutions into production at scale.

 

Making of Ampere Video

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Deep Learning Training

Up to 3X Higher AI Training on Largest Models

DLRM Training

Up to 3X Higher AI Training on Largest Models

DLRM on HugeCTR framework, precision = FP16 | ​NVIDIA A100 80GB batch size = 48 | NVIDIA A100 40GB batch size = 32 | NVIDIA V100 32GB batch size = 32.

​AI models are exploding in complexity as they take on next-level challenges such as conversational AI. Training them requires massive compute power and scalability.

NVIDIA A100 Tensor Cores with Tensor Float (TF32) provide up to 20X higher performance over the NVIDIA Volta with zero code changes and an additional 2X boost with automatic mixed precision and FP16. When combined with NVIDIA® NVLink®, NVIDIA NVSwitch™, PCI Gen4, NVIDIA® Mellanox® InfiniBand®, and the NVIDIA Magnum IO™ SDK, it’s possible to scale to thousands of A100 GPUs.

A training workload like BERT can be solved at scale in under a minute by 2,048 A100 GPUs, a world record for time to solution.

For the largest models with massive data tables like deep learning recommendation models (DLRM), A100 80GB reaches up to 1.3 TB of unified memory per node and delivers up to a 3X throughput increase over A100 40GB.

NVIDIA’s leadership in MLPerf, setting multiple performance records in the industry-wide benchmark for AI training.

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Deep Learning Inference

A100 introduces groundbreaking features to optimize inference workloads. It accelerates a full range of precision, from FP32 to INT4. Multi-Instance GPU (MIG) technology lets multiple networks operate simultaneously on a single A100 for optimal utilization of compute resources. And structural sparsity support delivers up to 2X more performance on top of A100’s other inference performance gains.

On state-of-the-art conversational AI models like BERT, A100 accelerates inference throughput up to 249X over CPUs.

On the most complex models that are batch-size constrained like RNN-T for automatic speech recognition, A100 80GB’s increased memory capacity doubles the size of each MIG and delivers up to 1.25X higher throughput over A100 40GB.

NVIDIA’s market-leading performance was demonstrated in MLPerf Inference. A100 brings 20X more performance to further extend that leadership.

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Up to 249X Higher AI Inference Performance
Over CPUs

BERT-LARGE Inference

Up to 249X Higher AI Inference Performance Over CPUs

BERT-Large Inference | CPU only: Dual Xeon Gold 6240 @ 2.60 GHz, precision = FP32, batch size = 128 | V100: NVIDIA TensorRT™ (TRT) 7.2, precision = INT8, batch size = 256 | A100 40GB and 80GB, batch size = 256, precision = INT8 with sparsity.​

Up to 1.25X Higher AI Inference Performance
Over A100 40GB

RNN-T Inference: Single Stream

Up to 1.25X Higher AI Inference Performance Over A100 40GB

MLPerf 0.7 RNN-T measured with (1/7) MIG slices. Framework: TensorRT 7.2, dataset = LibriSpeech, precision = FP16.

High-Performance Computing

To unlock next-generation discoveries, scientists look to simulations to better understand the world around us.

NVIDIA A100 introduces double precision Tensor Cores  to deliver the biggest leap in HPC performance since the introduction of GPUs. Combined with 80GB of the fastest GPU memory, researchers can reduce a 10-hour, double-precision simulation to under four hours on A100. HPC applications can also leverage TF32 to achieve up to 11X higher throughput for single-precision, dense matrix-multiply operations.

For the HPC applications with the largest datasets, A100 80GB’s additional memory delivers up to a 2X throughput increase with Quantum Espresso, a materials simulation. This massive memory and unprecedented memory bandwidth makes the A100 80GB the ideal platform for next-generation workloads.

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11X More HPC Performance in Four Years

Top HPC Apps​

11X More HPC Performance in Four Years

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.

Up to 1.8X Higher Performance for HPC Applications

Quantum Espresso​

Up to 1.8X Higher Performance for HPC Applications

Quantum Espresso measured using CNT10POR8 dataset, precision = FP64.

High-Performance Data Analytics

Up to 83X Faster than CPU, 2X Faster than A100 40GB on Big Data Analytics Benchmark

Up to 83X Faster than CPU, 2X Faster than A100 40GB on Big Data Analytics Benchmark

Big data analytics benchmark |  30 analytical retail queries, ETL, ML, NLP on 10TB dataset | CPU: Intel Xeon Gold 6252 2.10 GHz, Hadoop | V100 32GB, RAPIDS/Dask | A100 40GB and A100 80GB, RAPIDS/Dask/BlazingSQL​

Data scientists need to be able to analyze, visualize, and turn massive datasets into insights. But scale-out solutions are often bogged down by datasets scattered across multiple servers.

Accelerated servers with A100 provide the needed compute power—along with massive memory, over 2 TB/sec of memory bandwidth, and scalability with NVIDIA® NVLink® and NVSwitch™, —to tackle these workloads. Combined with InfiniBand, NVIDIA Magnum IO™ and the RAPIDS™ suite of open-source libraries, including the RAPIDS Accelerator for Apache Spark for GPU-accelerated data analytics, the NVIDIA data center platform accelerates these huge workloads at unprecedented levels of performance and efficiency.

On a big data analytics benchmark, A100 80GB delivered insights with 83X higher throughput than CPUs and a 2X increase over A100 40GB, making it ideally suited for emerging workloads with exploding dataset sizes.

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Enterprise-Ready Utilization

7X Higher Inference Throughput with Multi-Instance GPU (MIG)

BERT Large Inference

7X Higher Inference Throughput with Multi-Instance GPU (MIG)

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.​

Enterprise-Ready Utilization

A100 with MIG maximizes the utilization of GPU-accelerated infrastructure. With MIG, an A100 GPU can be partitioned into as many as seven independent instances, giving multiple users access to GPU acceleration. With A100 40GB, each MIG instance can be allocated up to 5GB, and with A100 80GB’s increased memory capacity, that size is doubled to 10GB.

MIG works with Kubernetes, containers, and hypervisor-based server virtualization. MIG lets infrastructure managers offer a right-sized GPU with guaranteed quality of service (QoS) for every job, extending the reach of accelerated computing resources to every user.

 

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Get the Most From Your Systems

An NVIDIA-Certified System, comprising of A100 and NVIDIA Mellanox SmartnNICs and DPUs is validated for performance, functionality, scalability, and security allowing enterprises to easily deploy complete solutions for AI workloads from the NVIDIA NGC catalog.

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Data Center GPUs

NVIDIA A100 for HGX

NVIDIA A100 for HGX

Ultimate performance for all workloads.

NVIDIA A100 for PCIe

NVIDIA A100 for PCIe

Highest versatility for all workloads.

Specifications

  A100 40GB PCIe A100 80GB PCIe A100 40GB SXM A100 80GB SXM
FP64 9.7 TFLOPS
FP64 Tensor Core 19.5 TFLOPS
FP32 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
Form Factor PCIe SXM
Interconnect NVIDIA® NVLink® Bridge for 2 GPUs: 600GB/s **
PCIe Gen4: 64GB/s		 NVLink: 600GB/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

* With sparsity
** SXM4 GPUs via HGX A100 server boards; PCIe GPUs via NVLink Bridge for up to two GPUs

 

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Inside the NVIDIA Ampere Architecture

Learn what’s new with the NVIDIA Ampere architecture and its implementation in the NVIDIA A100 GPU.

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