nvidia elite partnerNVIDIA GPU – Accelerating scientific discovery, visualizing big data for insights, and providing smart services to consumers are everyday challenges for researchers and engineers. Solving these challenges takes increasingly complex and precise simulations, the processing of tremendous amounts of data, or training sophisticated deep learning networks. These workloads also require accelerating data centers to meet the growing exponential demand for computing.

NVIDIA Ampere is the world’s leading platform for accelerated data centers, deployed by some of the world’s largest super-computing centers and enterprises. It combines GPU accelerators, accelerated computing systems, interconnect technologies, development tools, GPU applications and Compilers, like PGI to enable faster scientific discoveries and big data insights.

Ampere is incredibly fast for training and inference, and has the ability to fractionalize and partition itself from a single large GPU with maximum Scale-Up performance, or Scale-Out and partition itself in up to 7 independent GPUs to accelerate multiple smaller applications. The new Ampere architecture yields a new data center architecture for acceleration that is flexible, high throughput and enables higher utilization.

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For more than two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics. With a singular focus on this field, NVIDIA GPUs offers specialized platforms for the gaming, professional visualization, data center, GPU server and automotive markets. NVIDIA’s work is at the center of the most consequential mega-trends in GPU cluster technology — virtual reality, artificial intelligence and self-driving cars.

GPU servers have become an essential part in the computational research world. From bioinformatics to weather modeling, GPUs have offered over 70x speed up on researcher’s code. With hundreds of applications already accelerated by these cards, check to see if your favorite applications are on the GPU applications list.


8th Generation Data Center GPU for the Age of Elastic Computing

NVIDIA Ampere A100 Tensor Core GPU adds many new features while delivering significantly faster performance for HPC, AI, and data analytics workloads. Powered by the NVIDIA’s latest Ampere GPU architecture, The latest model, the A100, utilizes 3rd Gen Tensor Cores, Sparsity Acceleration, MIG (Multi-Instance GPUs) and 3rd Gen NVLINK & NVSWITCH.

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A100 Tensor Cores Accelerate HPC

The performance needs of HPC applications are growing rapidly. The A100 GPU supports Tensor operations that accelerate IEEE-compliant FP64 computations, delivering up to 2.5x the FP64 performance of the NVIDIA Tesla V100 GPU. Each SM in A100 computes a total of 64 FP64 FMA operations/clock (or 128 FP64 operations/clock), which is twice the throughput of Tesla V100.

Multi Instance GPUs

The new MIG feature can partition each A100 into as many as seven GPU Instances, each fully isolated with their own high-bandwidth memory, cache, and compute cores, for optimal utilization, effectively expanding access to every user and application. With NVIDIA Ampere architecture-based GPU, you can see and schedule jobs on their new virtual GPU instances as if they were physical GPUs.

AI Training and Inference

From scaling-up AI training and scientific computing, to scaling-out inference applications, to enabling real-time conversational AI, NVIDIA GPUs provide the necessary horsepower to accelerate numerous complex and unpredictable workloads. The NVIDIA A100 GPU delivers exceptional speedups over V100 for AI training and inference workloads


Perfectly Balanced. Blazing Performance.

Spearhead innovation from your desktop with the NVIDIA RTX™ A6000 and A5000 graphics cards, the perfect balance of power, performance, and reliability to tackle complex workflows. Built on the latest NVIDIA Ampere architecture and featuring 48 and 24 gigabytes (GB) of GPU memory, they’re everything designers, engineers, and artists need to realize their visions for the future, today.


Double Precision & Compute GPUs

Name A100
(80GB SXM)
(40GB SXM)
(80GB PCIe)
(40GB PCIe)
Appearance Image Image Image Image Image
Architecture Ampere Ampere Ampere Ampere Ampere
FP64 9.7 TF 9.7 TF 9.7 TF 9.7 TF 5.2 TF
FP64 Tensor Core 19.5 TF 19.5 TF 19.5 TF 19.5 TF 10.3 TF
FP32 19.5 TF 19.5 TF 19.5 TF 19.5 TF 10.3 TF
Tensor Float 32 (TF32) 156 TF | 312 TF* 156 TF | 312 TF* 156 TF | 312 TF* 156 TF | 312 TF* 82 TF | 165 TF*
BFLOAT16 Tensor Core 312 TF | 624 TF* 312 TF | 624 TF* 312 TF | 624 TF* 312 TF | 624 TF* 165 TF | 330 TF*
FP16 Tensor Core
INT8 Tensor Core 627 TOPS | 1248 TOPS* 626 TOPS | 1248 TOPS* 625 TOPS | 1248 TOPS* 624 TOPS | 1248 TOPS* 330 TOPS | 661 TOPS*
GPU Memory 80 GB HBM2e 40 GB HBM2 80 GB HBM2e 40 GB HBM2 24GB HBM2
GPU Memory Bandwidth 2,039 GB/s 1,555 GB/s 1,935 GB/s 1,555 GB/s 933 GB/s
TDP 400 W 400 W 300 W 250 W 165 W
Interconnect NVLink: 600GB/s
PCIe Gen4: 64GB/s
NVLink: 600GB/s
PCIe Gen4: 64GB/s
NVIDIA® NVLink® Bridge
for 2 GPUs: 600GB/s
PCIe Gen4: 64GB/s
NVIDIA® NVLink® Bridge
for 2 GPUs: 600GB/s
PCIe Gen4: 64GB/s
PCIe Gen4: 64GB/s

Single Precision & Visualization GPUs

Name A40 A6000 A5000 A4000 A2000
Appearance Image Image Image Image Image
Architecture Ampere Ampere Ampere Ampere Ampere
FP64 1,250 GF 867.8 GF 599 GF 125 GF
FP32 37.4 TF 38.7 TF 27.8 TF 19.2 TF 8 TF
Tensor Float 32 (TF32) 74.8 | 149.6* 309.7 TF 222.2 TF 153.4 TF 63.9 TF
BFLOAT16 Tensor Core 149.7 TF | 299.4
FP16 Tensor Core
INT8 Tensor Core 299.3 TOPS | 598.6 TOPS*
GPU Memory 48 GB GDDR6 with ECC 48 GB GDDR6 24 GB GDDR6 16 GB GDDR6 6 GB GDDR6
GPU Memory Bandwidth 696 GB/s 768 GB/s 768 GB/s 448 GB/s 288 GB/s
TDP 300 W 300 W 230 W 140 W 70 W
Interconnect NVIDIA® NVLink® 112.5 GB/s
PCIe Gen4 31.5 GB/s
NVLink: 112.5 GB/s
PCIe Gen4: 64GB/s
NVLink: 112.5 GB/s
PCIe Gen4: 64GB/s
PCIe Gen4: 64GB/s PCIe Gen4: 64GB/s

Virtualization GPUs

Name A16 A10 T4
Appearance Image Image Image
Architecture Ampere Ampere Turing
FP64 271.2 GF
FP32 8.678 TF 31.2 TF 8.1 TF
Tensor Float 32 (TF32) 62.5 TF | 125 TF* 65 TF
BFLOAT16 Tensor Core 8.678 TF 125 TF | 250 TF*
FP16 Tensor Core
INT8 Tensor Core 250 TOPS | 500 TOPS* 130 TOPS
GPU Memory 4x 16GB GDDR6 with ECC 24 GB GDDR6 16 GB GDDR6
GPU Memory Bandwidth 4x 232 GB/s 600 GB/s 300 GB/s
TDP 250 W 150 W 70 W
Interconnect PCI Express Gen 4 x16 PCIe Gen4: 64 GB/s PCIe 3.0 x 16

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Software Tools for GPU Computing

Tensorflow Artificial Intelligence Library

Tensorflow, developed by google, is an open source symbolic math library for high performance computation. It has quickly become an industry standard for artificial intelligence and machine learning applications, and is known for its flexibility, used in many scientific disciplines. It is based on the concept of a Tensor, which, as you may have guessed, is where the Volta Tensor Cores gets its name.

GPU Accelerated Libraries

There are a handful of GPU accelerated libraries that developers can use to speed up applications using GPUs. Many of them are NVIDIA CUDA libraries (such as cuBLAS and CUDA Math Library), but there are others such as IMSL Fortran libraries and HiPLAR (High Performance Linear Algebra in R). These libraries can be linked to replace standard libraries that are commonly used in non-GPU-Accelerated computing.

CUDA Development Toolkit

NVIDIA has created an entire toolkit devoted to computing on their CUDA-enabled GPUs. The CUDA toolkit, which includes the CUDA libraries, are the core of many GPU-Accelerated programs. CUDA is one of the most widely used toolkits in the GPGPU world today.

NVIDIA Deep Learning SDK

In today’s world, Deep Learning is becoming essential in many segments of the industry. For instance, Deep Learning is key in voice and image recognition where the machine must learn while gaining input. Writing algorithms for machines to learn from data is a difficult task, but NVIDIA has written a Deep Learning SDK to provide the tools necessary to help design code to run on GPUs.

OpenACC Parallel Programming Model

OpenACC is a user-driven directive-based performance-portable parallel programming model. It is designed for scientists and engineers interested in porting their codes to a wide-variety of heterogeneous HPC hardware platforms and architectures with significantly less programming effort than required with a low-level model. . The OpenACC Directives can be a powerful tool in porting a user’s application to run on GPU servers. There are two key features to OpenACC: easy of use and portability. Applications that use OpenACC can not only run on NVIDIA GPUs, but it can run on other GPUs, X86 CPUs & POWER CPUs, as well.

NVIDIA Accelerators dramatically lower data center costs by delivering exceptional performance with fewer, more powerful servers. This increased throughput means more scientific discoveries delivered to researchers every day.