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. The A100 80GB debuts the world’s fastest memory bandwidth at over 2 terabytes per second (TB/s) to run the largest models and datasets.
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.
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 . Representing the most powerful end-to-end AI and HPC platform for data centers, it allows researchers to rapidly deliver real-world results and deploy solutions into production at scale.
Deep Learning Training
Up to 3X Higher AI Training on Largest Models
DLRM Training
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 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 , NVIDIA , PCI Gen4, NVIDIA InfiniBand, and the 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 , setting multiple performance records in the industry-wide benchmark for AI training.
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 () 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 . A100 brings 20X more performance to further extend that leadership.
Up to 249X Higher AI Inference Performance
Over CPUs
BERT-LARGE Inference
BERT-Large Inference | CPU only: 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
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.
11X More HPC Performance in Four Years
Top HPC Apps
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
Quantum Espresso measured using CNT10POR8 dataset, precision = FP64.
High-Performance Data Analytics
2X Faster than A100 40GB on Big Data Analytics Benchmark
Big data analytics benchmark | 30 analytical retail queries, ETL, ML, NLP on 10TB dataset | 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 , —to tackle these workloads. Combined with InfiniBand, and the 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 a 2X increase over A100 40GB, making it ideally suited for emerging workloads with exploding dataset sizes.
Enterprise-Ready Utilization
7X Higher Inference Throughput with Multi-Instance GPU (MIG)
BERT Large Inference
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.
A100 with 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 . 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.
