Bitmain Antminer S19 Pro 110Th/s 3010W
April 8, 2024
NVIDIA L40 & L40S Enterprise 48GB
April 8, 2024
NVIDIA A100 Enterprise PCIe 40GB/80GB
The 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 high-performance computing (HPC) applications. 3-year manufacturer warranty included.
| CUDA Cores | 6912 |
|---|---|
| Streaming Multiprocessors | 108 |
| Tensor Cores | Gen 3 | 432 |
| GPU Memory | 40 GB or 80 GB HBM2e ECC on by Default |
| Memory Interface | 5120-bit |
| Memory Bandwidth | 1555 GB/s |
| NVLink | 2-Way, 2-Slot, 600 GB/s Bidirectional |
| MIG (Multi-Instance GPU) Support | Yes, up to 7 GPU Instances |
| FP64 | 9.7 TFLOPS |
| FP64 Tensor Core | 19.5 TFLOPS |
| FP32 | 19.5 TFLOPS |
| TF32 Tensor Core | 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* |
| Thermal Solutions | Passive |
| vGPU Support | NVIDIA Virtual Compute Server (vCS) |
| System Interface | PCIE 4.0 x16 |
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$7,600.00 – $13,200.00
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.
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 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® 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.
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.
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® 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 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 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.
| Memory | 40GB, 80GB |
|---|
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