NVIDIA accelerators for data centres

NVIDIA introduces the most powerful graphics accelerators on the market. These cards can accelerate many massively parallel tasks and scientific (HPC) applications or efficiently solve artificial intelligence (AI) algorithms.

NVIDIA A100

NVIDIA’s latest and most powerful HPC and AI card for data centres.

NVIDIA A30

The A30 is the latest successor to NVIDIA T4 accelerators and is most commonly used in accelerating HPC computing applications or artificial intelligence inference.

spec sheet

NVIDIA A16

The A16 accelerator combines the power of 4 graphics chips on a single board with 4x 16GB GDDR6 memory. This means support for up to 64 concurrent users on a single card in a dual-slot design.

spec sheet

NVIDIA A2

The NVIDIA A2 GPU provides entry-level inference with low power, a small footprint, and high performance for AI at the edge. Featuring a low-profile PCIe Gen4 card and a low 40-60W configurable thermal design power (TDP) capability, the A2 brings versatile inference acceleration to any server for deployment at scale.

spec sheet

NVIDIA A10

The A10 could be described as the younger sibling of the A40 accelerator. It will be used in visualization and virtualization. The compact single-slot A10 is suitable as an absolutely versatile card for a large number of tasks.

spec sheet

NVIDIA A40

It delivers unmatched performance in ray-traced rendering, high-performance virtual workstations, simulation, 3D design, VR and virtual production.

spec sheet

NVIDIA DGX systems

Supercomputers for solving machine learning and artificial intelligence problems. It is the most powerful hardware and fine-tuned software stack that includes the most widely used machine learning frameworks in the form of application containers.

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NVIDIA GPU Comparison for Data Centres

Parameter	NVIDIA A2	NVIDIA A16	NVIDIA A10	NVIDIA A40	NVIDIA A30	NVIDIA A100 SXM4 \| PCIe	DGX Station A100	DGX A100
Architecture	Ampere	Ampere	Ampere	Ampere	Ampere	Ampere	Ampere	Ampere
Card chip	GA107	GA107	GA102	GA102	GA100	GA100	GA100	GA100
# CUDA cores	1 280	4x 1 280	9 216	10 752	6 912	6 912	27 648	55 296
# Tensor cores	40	4x 40	288	336	224	432	1 728	3 456
FP64 (TFlops)	0,07	0,271	0,97	1,179	5,2	9,7	38,8	77,6
FP64 Tensor (TFlops)	—	—	—	—	10,3	19,5	78	156
FP32 (TFlops)	4,5	4x 4,5	31,2	37,4	10,3	19,5	78	156
TF32 Tensor (TFlops)	18*	4x 18*	125*	150*	165*	312*	1 248*	2 496*
FP16 Tensor (TFlops)	35,9*	4x 35,9*	250*	299*	330*	624*	2 496*	4 992*
INT8 Tensor (TOPS)	71,8*	4x 71,8*	500*	599*	661*	1248*	4 992*	9 994*
INT4 Tensor (TOPS)	144*	4x 144*	1000*	1197*	1321*	2496*	9 992*	19 968*
GPU memory	16 GB	4x 16 GB	24 GB	48 GB	24 GB	40 GB / 80 GB	160 / 320 GB	320 / 640 GB
Multi-Instance GPU	vGPU mode	vGPU mode	vGPU mode	vGPU mode	4 instance	7 instances	28 instances	56 instances
Memory technology	GDDR6	GDDR6	GDDR6	GDDR6	HBM2	HBM2	HBM2	HBM2
Memory throughput	200 GB/s	4x 200 GB/s	600 GB/s	696 GB/s	933 GB/s	1 ,5 / 2,0 TB/s	1 ,5 / 2,0 TB/s	1 ,5 / 2,0 TB/s
GPU link	PCIe 4	PCIe 4	PCIe 4	NVLink	NVLink 3	NVLink 3	NVLink 3	NVSwitch3, non-blocking, 4.8 TB/s
Power consumption	40-60 W	250 W	150 W	300 W	165 W	400 W \| 250 W	1 500 W	6,6 kW
Form factor	PCIe card	PCIe card	PCIe card	PCIe card	PCIe card	SXM4 \| PCIe	tower, water cooling CPU and GPU	rack, 6U
PCIe generation	PCIe gen4	PCIe gen4	PCIe gen4	PCIe gen4	PCIe gen4	PCI gen4	PCIe gen4	PCIe gen4
Year of launch	2021	2021	2021	2020	2021	2020	2020	2020
Card	NVIDIA A2	NVIDIA A16	NVIDIA A10	NVIDIA A40	NVIDIA A30	NVIDIA A100 SXM4	DGX Station A100	DGX A100
	spec sheet	spec sheet	spec sheet	spec sheet	spec sheet	spec sheet	spec sheet	spec sheet

* the stated power is for calculations with sparse nuts (Sparsity), for standard calculations the power is half of the specified values
** NVIDIA A100 PCIe achieves 90% of the stated computing power

GPU Accelerators for data centres

NVIDIA Tesla and Ampere graphics accelerators are designed to accelerate HPC applications or deploy artificial intelligence and deep learning algorithms.

Key benefits of NVIDIA cards include dedicated Tensor cores for machine learning applications. And large memory (up to 80 GB per accelerator), secured by ECC technology. To enable the accelerators to communicate quickly with each other, NVIDIA has connected them with a special interface with huge data throughput – NVLink. NVLink achieves transfer rates of up to 600 GB/s. In addition, the NVIDIA DGX A100 offers a super-powerful NVSwitch. This provides a total throughput between eight NVIDIA Ampere A100 cards of up to 4.8 TB/s.

Intersect360 Research analysis shows that most of the most widely used HPC applications already support NVIDIA cards. These include These include GROMACS, Ansys Fluent, Gaussian, VASP, NAMD, Abaqus, OpenFoam, LS Dyna, BLAST, Amber, Gamess, ParaView, NASTRAN and many others. The large expansion of NVIDIA accelerators has been helped by support for deep learning frameworks – TensorFlow, Caffe, PyTorch, MXNET, Chainer, Keras and many more.

The graph on the right shows how fast the graphics accelerator field is evolving, with a ninefold increase in performance in just four years. The figures are based on the average of benchmark results for the most widely used AI and HPC applications (Amber, Chroma, GROMACS, MILC, NAMD, PyTorch, Quantum Espresso, TensorFlow and VASP), measured on dual-socket servers with four P100, V100 or A100 accelerators each.

NVIDIA GP GPU Performance Evolution in Time

How to choose the best GPU?

The current GP GPU cards for data centers and their typical deployments are mentioned in the infographics.

NVIDIA visualization cards

NVIDIA RTX professional cards are designed primarily for graphics processing and simulation, machine learning, data analytics, and high-performance workstation virtualization.

Comparison of Nvidia cards for visualization

Parameter	RTX 3080	RTX 3090	RTX A2000	RTX A4000	RTX A4500	RTX A5000	RTX A6000
Architecture	Ampere	Ampere	Ampere	Ampere	Ampere	Ampere	Ampere
Card chip	GA102	GA102	GA106	GA104	GA102	GA102	GA102
# CUDA cores	8704	10 496	3 328	6 144	7 168	8 192	10 752
# Tensor cores	272	328	104	192	224	256	336
FP64 (TFlops)	0,47	0,56	0,124	0,6	0,739	0,87	1,25
FP32 (TFlops)	29,8	35.6	8	19,2	23,65	27,7	40
FP16 Tensor (TFlops)	119/238*	142/284*	63,9*	153,4*	189,2	222,2*	309,7*
GPU memory	10 GB	24 GB	6 / 12 GB	16 GB	20 GB	24 GB	48 GB
Memory	GDDR6X	GDDR6X	GDDR6	GDDR6	GDDR6	GDDR6	GDDR6
Memory throughput	760 GB / s	936 GB / s	288 GB / s	448 GB / s	640 GB / s	768 GB / s	768 GB / s
ECC memory	none	none	ECC	ECC	ECC	ECC	ECC
GPU link	PCIe gen4	NVLink 2-way	PCIe gen4	PCIe gen4	NVLink 2-way	NVLink 2-way	NVLink 2-way
Max. power consumption	320 W	350 W	70 W	140 W	200 W	230 W	300 W
Form factor	PCIe gen4	PCIe gen4	PCIe gen4	PCIe gen4	PCIe gen4	PCIe gen4	PCIe gen4
For data centres**	No	No	Yes	Yes	Yes	Yes	Yes
Announcement year	2020	2020	2021	2021	2021	2021	2020
Card	RTX 3080	RTX 3090	RTX A2000	RTX A4000	RTX A4500	RTX A5000	RTX A6000
	spec sheet	spec sheet	spec sheet	spec sheet	spec sheet	spec sheet	spec sheet

* uvedený výkon je pro výpočty s řídkými maticemi (Sparcity), pro standardní výpočty je výkon poloviční oproti uvedeným hodnotám

** podle Nvidia licenčních podmínek k ovladačům karet (EULA) nejsou grafické karty GeForce (GTX, RTX) určeny pro datová centra:
“No Datacenter Deployment. The SOFTWARE is not licensed for datacenter deployment, except that blockchain processing in a datacenter is permitted.”
zdroj: https://www.nvidia.com/content/DriverDownload-March2009/licence.php?lang=us&type=GeForce

NVIDIA offers special pricing and project-specific programs on both GPU and DGX systems, plus support for educational institutions (EDUs) and start-ups.

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Testing

To test the performance and especially the speed of deploying ML and AI applications, we have the NVIDIA DGX Station and, as part of the NVIDIA Test Drive program, the NVIDIA A100, NVIDIA A40, NVIDIA A10, Tesla V100 or Tesla T4 accelerators. If you are interested in our testing offer, please fill out this form.