NVIDIA GPU – One Platform. Unlimited Data Center Acceleration.

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NVIDIA 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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The Exponential Growth of GPU Computing

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.

NVIDIA Ampere A100

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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NVIDIA Volta V100

Three Reasons to Upgrade to the NVIDIA Ampere A100

Accelerating HPC

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 GPU (MIG)

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.

A100 Unified AI Acceleration

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

Three Reasons to Upgrade to the NVIDIA Ampere A100

Nvidia RTX A6000 and A5000 GPUs

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.

Understanding NVIDIA’s product line

Tesla? Volta? GeForce? Turing? Pascal? NVIDIA has a very diverse product line which some find challenging to navigate. While you should contact an expert to determine your specific needs, here is a simplified rundown of some of NVIDIA’s product line to help you understand some of the basics.


Tesla GPUs are aimed at data center compute

The Tesla brand of products is geared to the Data Center. These are highly specialized cards for compute, and as such, are the GPU of choice for data centers and supercomputers. They do not have video outputs, for example, and often utilize passive cooling. If you are using GPUs for clusters or for compute, you want a Tesla card. As such, Aspen Systems recommends the Tesla series for HPC.


Quadro GPUs are aimed at data science and advanced graphic applications

Quadro cards are usually more powerful than GeForce cards, but are similar, and in some cases have nearly identical specifications and core technology. However, you get something much more valuable whenever you purchase a card with the name “Quadro” on it — World-class support by NVIDIA. In mission-critical applications, the name “Quadro” can shorten downtime and get you up and running fast.



GeForce GPUs are aimed at consumer market and gaming

You may have heard of GeForce cards before, or have seen them on the shelves at your local consumer electronics store. These cards are consumer-oriented cards, and are usually used for gaming applications and displays. Aspen Systems does not recommend GeForce GPUs for HPC, data science, artificial intelligence, machine learning, or any other compute-intensive applications, as they are not specialized for that purpose.


The architecture refers to the generation of technology used in the card, and each new generation usually introduces something new to the mix.


(EG A100, A100 PCIe). The NVIDIA Ampere architecture increases throughput of scale up applications and now provides flexibility to accelerate scale out applications with 3rd generation Tensor Cores, Multi-Instance GPUs (MIG), and 3rd generation NVLink connections to accelerate AI and High-Performance Computing (HPC).


(EG RTX 8000, RTX 6000, Titan RTX). The Turing generation of NVIDIA technology is focused on graphics. Turing architecture introduced the new RT cores that offer real-time ray-tracing directly through the hardware rather than the more cycle-hungry process of software ray-tracing.


(EG V100, GV100). Volta introduced the new Tensor cores into the mix. These cores are a huge leap forward for applications involving artificial intelligence and machine learning, as they are highly specialized for tasks associated with the “tensors” used in such applications and libraries, such as TensorFlow, and are designed from the ground up to handle these workloads.

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

* With Sparcity

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

* With Sparcity

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

* With Sparcity

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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

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.

Deep Learning SDK

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.