Nvidia Tesla

Last updated

Nvidia Tesla
NvidiaTesla.jpg
Manufacturer Nvidia
IntroducedMay 2, 2007;
17 years ago (2007-05-02)
DiscontinuedThe brand Tesla discontinued on May 2020;4 years ago (2020-05), now branded as Nvidia Data Center GPUs
TypeGeneral purpose graphics cards

Nvidia Tesla is the former name for a line of products developed by Nvidia targeted at stream processing or general-purpose graphics processing units (GPGPU), named after pioneering electrical engineer Nikola Tesla. Its products began using GPUs from the G80 series, and have continued to accompany the release of new chips. They are programmable using the CUDA or OpenCL APIs.

Contents

The Nvidia Tesla product line competed with AMD's Radeon Instinct and Intel Xeon Phi lines of deep learning and GPU cards.

Nvidia retired the Tesla brand in May 2020, reportedly because of potential confusion with the brand of cars. [1] Its new GPUs are branded Nvidia Data Center GPUs [2] as in the Ampere-based A100 GPU. [3]

Nvidia DGX servers feature Nvidia GPGPUs.

Overview

Nvidia Tesla C2075 NvidiaTesla2075.JPG
Nvidia Tesla C2075

Offering computational power much greater than traditional microprocessors, the Tesla products targeted the high-performance computing market. [4] As of 2012, Nvidia Teslas power some of the world's fastest supercomputers, including Summit at Oak Ridge National Laboratory and Tianhe-1A, in Tianjin, China.

Tesla cards have four times the double precision performance of a Fermi-based Nvidia GeForce card of similar single precision performance.[ citation needed ] Unlike Nvidia's consumer GeForce cards and professional Nvidia Quadro cards, Tesla cards were originally unable to output images to a display. However, the last Tesla C-class products included one Dual-Link DVI port. [5]

Applications

Tesla products are primarily used in simulations and in large-scale calculations (especially floating-point calculations), and for high-end image generation for professional and scientific fields. [6]

In 2013, the defense industry accounted for less than one-sixth of Tesla sales, but Sumit Gupta predicted increasing sales to the geospatial intelligence market. [7]

Specifications

Model Micro-
architecture 		LaunchChipsCore clock
(MHz)		ShadersMemoryProcessing power (GFLOPS) [lower-alpha 1] CUDA
compute
capability [lower-alpha 2] 		TDP
(W)		Notes, form factor
CUDA cores
(total)		Base clock (MHz)Max boost
clock (MHz) [lower-alpha 3] 		Bus typeBus width
(bit)		Size
(GB)		Clock
(MT/s)		Bandwidth
(GB/s)		 Half precision
Tensor Core FP32 Accumulate		 Single precision
(MAD or FMA)		 Double precision
(FMA)
C870 GPU Computing Module [lower-alpha 4] Tesla May 2, 20071× G806001281,350—GDDR33841.51,60076.8No345.6No1.0170.9Internal PCIe GPU (full-height, dual-slot)
D870 Deskside Computer [lower-alpha 4] May 2, 20072× G806002561,350—GDDR32× 3842× 1.51,6002× 76.8No691.2No1.0520Deskside or 3U rack-mount external GPUs
S870 GPU Computing Server [lower-alpha 4] May 2, 20074× G806005121,350—GDDR34× 3844× 1.51,6004× 76.8No1382.4No1.01U rack-mount external GPUs, connect via 2× PCIe (×16)
C1060 GPU Computing Module [lower-alpha 5] April 9, 20091× GT2006022401,296 [9] —GDDR351241,600102.4No622.0877.761.3187.8Internal PCIe GPU (full-height, dual-slot)
S1070 GPU Computing Server "400 configuration" [lower-alpha 5] June 1, 20084× GT2006029601296—GDDR34× 5124× 41,538.44× 98.5No2,488.3311.01.38001U rack-mount external GPUs, connect via 2× PCIe (×8 or ×16)
S1070 GPU Computing Server "500 configuration" [lower-alpha 5] 1,440—No2,764.8345.6
S1075 GPU Computing Server [lower-alpha 5] [10] June 1, 20084× GT2006029601,440—GDDR34× 5124× 41,538.44× 98.5No2,764.8345.61.31U rack-mount external GPUs, connect via 1× PCIe (×8 or ×16)
Quadro Plex 2200 D2 Visual Computing System [lower-alpha 6] July 25, 20082× GT200GL6484801,296—GDDR32× 5122× 41,6002× 102.4No1,244.2155.51.3Deskside or 3U rack-mount external GPUs with 4 dual-link DVI outputs
Quadro Plex 2200 S4 Visual Computing System [lower-alpha 6] July 25, 20084× GT200GL6489601,296—GDDR34× 5124× 41,6004× 102.4No2,488.3311.01.31,2001U rack-mount external GPUs, connect via 2× PCIe (×8 or ×16)
C2050 GPU Computing Module [11] Fermi July 25, 20111× GF1005754481,150—GDDR53843 [lower-alpha 7] 3000144No1,030.4515.22.0247Internal PCIe GPU (full-height, dual-slot)
M2050 GPU Computing Module [12] July 25, 2011—3,092148.4No225
C2070 GPU Computing Module [11] July 25, 20111× GF1005754481,150—GDDR53846 [lower-alpha 7] 3,000144No1,030.4515.22.0247Internal PCIe GPU (full-height, dual-slot)
C2075 GPU Computing Module [13] July 25, 2011—3,000144No225
M2070/M2070Q GPU Computing Module [14] July 25, 2011—3,132150.336No225
M2090 GPU Computing Module [15] July 25, 20111× GF1106505121,300—GDDR53846 [lower-alpha 7] 3700177.6No1,331.2665.62.0225Internal PCIe GPU (full-height, dual-slot)
S2050 GPU Computing ServerJuly 25, 20114× GF10057517921150—GDDR54× 3844× 3 [lower-alpha 7] 34× 148.4No4,121.62,060.82.09001U rack-mount external GPUs, connect via 2× PCIe (×8 or ×16)
S2070 GPU Computing Server—4× 6 [lower-alpha 7] No
K10 GPU accelerator [16] Kepler May 1, 20122× GK104—3,072745 ?GDDR52× 2562× 45,0002× 160No4,577190.73.0225Internal PCIe GPU (full-height, dual-slot)
K20 GPU accelerator [17] [18] November 12, 20121× GK110—2,496706758GDDR532055,200208No3,5241,1753.5225Internal PCIe GPU (full-height, dual-slot)
K20X GPU accelerator [19] November 12, 20121× GK110—2,688732 ?GDDR538465,200250No3,9351,3123.5235Internal PCIe GPU (full-height, dual-slot)
K40 GPU accelerator [20] October 8, 20131× GK110B—2,880745875GDDR538412 [lower-alpha 7] 6,000288No4,291–5,0401,430–1,6803.5235Internal PCIe GPU (full-height, dual-slot)
K80 GPU accelerator [21] November 17, 20142× GK210—4,992560875GDDR52× 3842× 125,0002× 240No5,591–8,7361,864–2,9123.7300Internal PCIe GPU (full-height, dual-slot)
M4 GPU accelerator [22] [23] Maxwell November 10, 20151× GM206—1,0248721,072GDDR512845,50088No1,786–2,19555.81–68.615.250–75Internal PCIe GPU (half-height, single-slot)
M6 GPU accelerator [24] August 30, 20151× GM204-995-A1—15367221,051GDDR525684,600147.2No2,218–3,22969.3–100.95.275–100Internal MXM GPU
M10 GPU accelerator [25] 4× GM107—2,5601,033 ?GDDR54× 1284× 85,1884× 83No5,289165.35.2225Internal PCIe GPU (full-height, dual-slot)
M40 GPU accelerator [23] [26] November 10, 20151× GM200—3,0729481,114GDDR538412 or 246,000288No5,825–6,844182.0–213.95.2250Internal PCIe GPU (full-height, dual-slot)
M60 GPU accelerator [27] August 30, 20152× GM204-895-A1—4,0968991,178GDDR52× 2562× 85,0002× 160No7,365–9,650230.1–301.65.2225–300Internal PCIe GPU (full-height, dual-slot)
P4 GPU accelerator [28] Pascal September 13, 20161× GP104—2,5608101,063GDDR525686,000192.0No4,147–5,443129.6–170.16.150-75 PCIe card
P6 GPU accelerator [29] [30] March 24, 20171× GP104-995-A1—2,0481,0121,506GDDR5256163,003192.2No6,169192.86.190 MXM card
P40 GPU accelerator [28] September 13, 20161× GP102—3,8401,3031,531GDDR5384247,200345.6No10,007–11,758312.7–367.46.1250 PCIe card
P100 GPU accelerator (mezzanine) [31] [32] April 5, 20161× GP100-890-A1—3,5841,3281,480 HBM2 4,096161,430732No9,519–10,6094,760–5,3046.0300 SXM card
P100 GPU accelerator (16 GB card) [33] June 20, 20161× GP100—11261303No8,071‒9,3404,036‒4,670250 PCIe card
P100 GPU accelerator (12 GB card) [33] June 20, 2016—3,07212549No8,071‒9,3404,036‒4,670
V100 GPU accelerator (mezzanine) [34] [35] [36] Volta May 10, 20171× GV100-895-A1—5120Un­known1,455HBM24,09616 or 321,750900119,19214,8997,4507.0300 SXM card
V100 GPU accelerator (PCIe card) [34] [35] [36] June 21, 20171× GV100—Un­known1,370112,22414,0287,014250PCIe card
V100 GPU accelerator (PCIe FHHL card)March 27, 20181× GV100—9371,290161,620829.44105,68013,2106,605250PCIe FHHL card
T4 GPU accelerator (PCIe card) [37] [38] Turing September 12, 20181× TU104-895-A1—2,5605851,590GDDR6256165,00032064,8008,100Un­known7.570PCIe card
A2 GPU accelerator (PCIe card) [39] Ampere November 10, 20211× GA107—1,2801,4401,770GDDR6128166,25220018,1244,5311408.640-60PCIe card (half height, single-slot)
A10 GPU accelerator (PCIe card) [40] April 12, 20211× GA102-890-A1—9,2168851,695GDDR6384246,252600124,96031,2409768.6150PCIe card (single-slot)
A16 GPU accelerator (PCIe card) [41] April 12, 20214× GA107—4× 1,2808851,695GDDR64× 1284× 167,2424× 2004x 18,4324× 4,6081,084.88.6250PCIe card (dual-slot)
A30 GPU accelerator (PCIe card) [42] April 12, 20211× GA100—3,5849301,440HBM23,072241,215933.1165,12010,3205,1618.0165PCIe card (dual-slot)
A40 GPU accelerator (PCIe card) [43] October 5, 20201× GA102—10,7521,3051,740GDDR6384487,248695.8149,68037,4201,1688.6300PCIe card (dual-slot)
A100 GPU accelerator (PCIe card) [44] [45] May 14, 2020 [46] 1× GA100-883AA-A1—6,9127651410HBM25,12040 or 801,2151,555312,00019,5009,7008.0250PCIe card (dual-slot)
H100 GPU accelerator (PCIe card) [47] Hopper March 22, 2022 [48] 1× GH100 [49] —14,5921,0651,755 CUDA 1620 TCHBM2E5120801,0002,039756,44951,20025,6009.0350PCIe card (dual-slot)
H100 GPU accelerator (SXM card)—16,8961,0651,980 CUDA 1,830 TCHBM35,120801,5003,352989,43066,90033,5009.0700 SXM card
L40 GPU accelerator [50] Ada Lovelace October 13, 20221× AD102 [51] —18,1767352,490GDDR6384482,250864362,06690,5161,4148.9300PCIe card (dual-slot)
L4 GPU accelerator [52] [53] March 21, 2023 [54] 1x AD104 [55] —7,4247952,040GDDR6192241,563300121,00030,3004908.972HHHL single slot PCIe card

Notes

  1. ↑ To calculate the processing power see Tesla (microarchitecture)#Performance, Fermi (microarchitecture)#Performance, Kepler (microarchitecture)#Performance, Maxwell (microarchitecture)#Performance, or Pascal (microarchitecture)#Performance. A number range specifies the minimum and maximum processing power at, respectively, the base clock and maximum boost clock.
  2. ↑ Core architecture version according to the CUDA programming guide.
  3. ↑ GPU Boost is a default feature that increases the core clock rate while remaining under the card's predetermined power budget. Multiple boost clocks are available, but this table lists the highest clock supported by each card. [8]
  4. 1 2 3 Specifications not specified by Nvidia assumed to be based on the GeForce 8800 GTX
  5. 1 2 3 4 Specifications not specified by Nvidia assumed to be based on the GeForce GTX 280
  6. 1 2 Specifications not specified by Nvidia assumed to be based on the Quadro FX 5800
  7. 1 2 3 4 5 6 With ECC on, a portion of the dedicated memory is used for ECC bits, so the available user memory is reduced by 12.5%. (e.g. 4 GB total memory yields 3.5 GB of user available memory.)

See also

Related Research Articles

<span class="mw-page-title-main">GeForce</span> Brand of GPUs by Nvidia

GeForce is a brand of graphics processing units (GPUs) designed by Nvidia and marketed for the performance market. As of the GeForce 40 series, there have been eighteen iterations of the design. The first GeForce products were discrete GPUs designed for add-on graphics boards, intended for the high-margin PC gaming market, and later diversification of the product line covered all tiers of the PC graphics market, ranging from cost-sensitive GPUs integrated on motherboards, to mainstream add-in retail boards. Most recently, GeForce technology has been introduced into Nvidia's line of embedded application processors, designed for electronic handhelds and mobile handsets.

<span class="mw-page-title-main">Graphics processing unit</span> Specialized electronic circuit; graphics accelerator

A graphics processing unit (GPU) is a specialized electronic circuit initially designed to accelerate computer graphics and image processing. After their initial design, GPUs were found to be useful for non-graphic calculations involving embarrassingly parallel problems due to their parallel structure. Other non-graphical uses include the training of neural networks and cryptocurrency mining.

<span class="mw-page-title-main">Quadro</span> Brand of Nvidia graphics cards used in workstations

Quadro was Nvidia's brand for graphics cards intended for use in workstations running professional computer-aided design (CAD), computer-generated imagery (CGI), digital content creation (DCC) applications, scientific calculations and machine learning from 2000 to 2020.

<span class="mw-page-title-main">CUDA</span> Parallel computing platform and programming model

Compute Unified Device Architecture (CUDA) is a proprietary parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs (GPGPU). CUDA API and its runtime: The CUDA API is an extension of the C programming language that adds the ability to specify thread-level parallelism in C and also to specify GPU device specific operations (like moving data between the CPU and the GPU). CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.

<span class="mw-page-title-main">Tegra</span> System on a chip by Nvidia

Tegra is a system on a chip (SoC) series developed by Nvidia for mobile devices such as smartphones, personal digital assistants, and mobile Internet devices. The Tegra integrates an ARM architecture central processing unit (CPU), graphics processing unit (GPU), northbridge, southbridge, and memory controller onto one package. Early Tegra SoCs are designed as efficient multimedia processors. The Tegra-line evolved to emphasize performance for gaming and machine learning applications without sacrificing power efficiency, before taking a drastic shift in direction towards platforms that provide vehicular automation with the applied "Nvidia Drive" brand name on reference boards and its semiconductors; and with the "Nvidia Jetson" brand name for boards adequate for AI applications within e.g. robots or drones, and for various smart high level automation purposes.

<span class="mw-page-title-main">GeForce 400 series</span> Series of GPUs by Nvidia

The GeForce 400 series is a series of graphics processing units developed by Nvidia, serving as the introduction of the Fermi microarchitecture. Its release was originally slated in November 2009, however, after delays, it was released on March 26, 2010, with availability following in April 2010.

Graphics Core Next (GCN) is the codename for a series of microarchitectures and an instruction set architecture that were developed by AMD for its GPUs as the successor to its TeraScale microarchitecture. The first product featuring GCN was launched on January 9, 2012.

<span class="mw-page-title-main">GeForce 700 series</span> Series of GPUs by Nvidia

The GeForce 700 series is a series of graphics processing units developed by Nvidia. While mainly a refresh of the Kepler microarchitecture, some cards use Fermi (GF) and later cards use Maxwell (GM). GeForce 700 series cards were first released in 2013, starting with the release of the GeForce GTX Titan on February 19, 2013, followed by the GeForce GTX 780 on May 23, 2013. The first mobile GeForce 700 series chips were released in April 2013.

<span class="mw-page-title-main">GeForce 900 series</span> Series of GPUs by Nvidia

The GeForce 900 series is a family of graphics processing units developed by Nvidia, succeeding the GeForce 700 series and serving as the high-end introduction to the Maxwell microarchitecture, named after James Clerk Maxwell. They are produced with TSMC's 28 nm process.

<span class="mw-page-title-main">Kepler (microarchitecture)</span> GPU microarchitecture by Nvidia

Kepler is the codename for a GPU microarchitecture developed by Nvidia, first introduced at retail in April 2012, as the successor to the Fermi microarchitecture. Kepler was Nvidia's first microarchitecture to focus on energy efficiency. Most GeForce 600 series, most GeForce 700 series, and some GeForce 800M series GPUs were based on Kepler, all manufactured in 28 nm. Kepler found use in the GK20A, the GPU component of the Tegra K1 SoC, and in the Quadro Kxxx series, the Quadro NVS 510, and Tesla computing modules.

<span class="mw-page-title-main">NVLink</span> High speed chip interconnect

NVLink is a wire-based serial multi-lane near-range communications link developed by Nvidia. Unlike PCI Express, a device can consist of multiple NVLinks, and devices use mesh networking to communicate instead of a central hub. The protocol was first announced in March 2014 and uses a proprietary high-speed signaling interconnect (NVHS).

<span class="mw-page-title-main">Pascal (microarchitecture)</span> GPU microarchitecture by Nvidia

Pascal is the codename for a GPU microarchitecture developed by Nvidia, as the successor to the Maxwell architecture. The architecture was first introduced in April 2016 with the release of the Tesla P100 (GP100) on April 5, 2016, and is primarily used in the GeForce 10 series, starting with the GeForce GTX 1080 and GTX 1070, which were released on May 17, 2016, and June 10, 2016, respectively. Pascal was manufactured using TSMC's 16 nm FinFET process, and later Samsung's 14 nm FinFET process.

<span class="mw-page-title-main">Volta (microarchitecture)</span> GPU microarchitecture by Nvidia

Volta is the codename, but not the trademark, for a GPU microarchitecture developed by Nvidia, succeeding Pascal. It was first announced on a roadmap in March 2013, although the first product was not announced until May 2017. The architecture is named after 18th–19th century Italian chemist and physicist Alessandro Volta. It was Nvidia's first chip to feature Tensor Cores, specially designed cores that have superior deep learning performance over regular CUDA cores. The architecture is produced with TSMC's 12 nm FinFET process. The Ampere microarchitecture is the successor to Volta.

<span class="mw-page-title-main">Nvidia DGX</span> Line of Nvidia produced servers and workstations

The Nvidia DGX represents a series of servers and workstations designed by Nvidia, primarily geared towards enhancing deep learning applications through the use of General-Purpose computing on Graphics Processing Units (GPGPU). These systems typically come in a rackmount format featuring high-performance x86 server CPUs on the motherboard.

<span class="mw-page-title-main">AMD Instinct</span> Brand name by AMD; data center GPUs for high-performance-computing, machine learning

AMD Instinct is AMD's brand of data center GPUs. It replaced AMD's FirePro S brand in 2016. Compared to the Radeon brand of mainstream consumer/gamer products, the instinct product line is intended to accelerate deep learning, artificial neural network, and high-performance computing/GPGPU applications.

<span class="mw-page-title-main">Turing (microarchitecture)</span> GPU microarchitecture by Nvidia

Turing is the codename for a graphics processing unit (GPU) microarchitecture developed by Nvidia. It is named after the prominent mathematician and computer scientist Alan Turing. The architecture was first introduced in August 2018 at SIGGRAPH 2018 in the workstation-oriented Quadro RTX cards, and one week later at Gamescom in consumer GeForce 20 series graphics cards. Building on the preliminary work of Volta, its HPC-exclusive predecessor, the Turing architecture introduces the first consumer products capable of real-time ray tracing, a longstanding goal of the computer graphics industry. Key elements include dedicated artificial intelligence processors and dedicated ray tracing processors. Turing leverages DXR, OptiX, and Vulkan for access to ray tracing. In February 2019, Nvidia released the GeForce 16 series GPUs, which utilizes the new Turing design but lacks the RT and Tensor cores.

<span class="mw-page-title-main">Ampere (microarchitecture)</span> GPU microarchitecture by Nvidia

Ampere is the codename for a graphics processing unit (GPU) microarchitecture developed by Nvidia as the successor to both the Volta and Turing architectures. It was officially announced on May 14, 2020 and is named after French mathematician and physicist André-Marie Ampère.

<span class="mw-page-title-main">Intel Xe</span> Intel GPU architecture

Intel Xe, earlier known unofficially as Gen12, is a GPU architecture developed by Intel.

<span class="mw-page-title-main">CDNA (microarchitecture)</span> AMD compute-focused GPU microarchitecture

CDNA is a compute-centered graphics processing unit (GPU) microarchitecture designed by AMD for datacenters. Mostly used in the AMD Instinct line of data center graphics cards, CDNA is a successor to the Graphics Core Next (GCN) microarchitecture; the other successor being RDNA, a consumer graphics focused microarchitecture.

References

  1. ↑ Casas, Alex (19 May 2020). "NVIDIA Drops Tesla Brand To Avoid Confusion With Tesla". Wccftech. Retrieved 8 July 2020.
  2. ↑ "NVIDIA Supercomputing Solutions".
  3. ↑ "NVIDIA A100 GPUs Power the Modern Data Center". NVIDIA. Retrieved 8 July 2020.
  4. ↑ "High Performance Computing - Supercomputing with Tesla GPUs".
  5. ↑ "Professional Workstation Solutions".
  6. ↑ Tesla Technical Brief (PDF)
  7. ↑ "Nvidia chases defense, intelligence ISVs with GPUs". www.theregister.com. Retrieved 8 July 2020.
  8. ↑ "Nvidia GPU Boost For Tesla" (PDF). January 2014. Retrieved 7 December 2015.
  9. ↑ "Tesla C1060 Computing Processor Board" (PDF). Nvidia.com. Retrieved 11 December 2015.
  10. ↑ "Difference between Tesla S1070 and S1075". 31 October 2008. Retrieved 29 January 2017. S1075 has one interface card
  11. 1 2 "Tesla C2050 and Tesla C2070 Computing Processor" (PDF). Nvidia.com. Retrieved 11 December 2015.
  12. ↑ "Tesla M2050 and Tesla M2070/M2070Q Dual-Slot Computing Processor Modules" (PDF). Nvidia.com. Retrieved 11 December 2015.
  13. ↑ "Tesla C2075 Computing Processor Board" (PDF). Nvidia.com. Retrieved 11 December 2015.
  14. ↑ Hand, Randall (23 August 2010). "NVidia Tesla M2050 & M2070/M2070Q Specs OnlineVizWorld.com". VizWorld.com. Retrieved 11 December 2015.
  15. ↑ "Tesla M2090 Dual-Slot Computing Processor Module" (PDF). Nvidia.com. Retrieved 11 December 2015.
  16. ↑ "Tesla K10 GPU accelerator" (PDF). Nvidia.com. Retrieved 11 December 2015.
  17. ↑ "Tesla K20 GPU active accelerator" (PDF). Nvidia.com. Retrieved 11 December 2015.
  18. ↑ "Tesla K20 GPU accelerator" (PDF). Nvidia.com. Retrieved 11 December 2015.
  19. ↑ "Tesla K20X GPU accelerator" (PDF). Nvidia.com. Retrieved 11 December 2015.
  20. ↑ "Tesla K40 GPU accelerator" (PDF). Nvidia.com. Retrieved 11 December 2015.
  21. ↑ "Tesla K80 GPU accelerator" (PDF). Images.nvidia.com. Retrieved 11 December 2015.
  22. ↑ "Nvidia Announces Tesla M40 & M4 Server Cards - Data Center Machine Learning". Anandtech.com. Retrieved 11 December 2015.
  23. 1 2 "Accelerating Hyperscale Datacenter Applications with Tesla GPUs | Parallel Forall". Devblogs.nvidia.com. 10 November 2015. Retrieved 11 December 2015.
  24. ↑ "Tesla M6" (PDF). Images.nvidia.com. Retrieved 28 May 2016.
  25. ↑ "Tesla M10" (PDF). Images.nvidia.com. Retrieved 29 October 2016.
  26. ↑ "Tesla M40" (PDF). Images.nvidia.com. Retrieved 11 December 2015.
  27. ↑ "Tesla M60" (PDF). Images.nvidia.com. Retrieved 27 May 2016.
  28. 1 2 Smith, Ryan (13 September 2016). "Nvidia Announces Tesla P40 & Tesla P4 - Network Inference, Big & Small". Anandtech. Retrieved 13 September 2016.
  29. ↑ "Tesla P6" (PDF). www.nvidia.com. Retrieved 7 March 2019.
  30. ↑ "Tesla P6 Specs". www.techpowerup.com. Retrieved 7 March 2019.
  31. ↑ Smith, Ryan (5 April 2016). "Nvidia Announces Tesla P100 Accelerator - Pascal GP100 for HPC". Anandtech.com. Anandtech.com. Retrieved 5 April 2016.
  32. ↑ Harris, Mark. "Inside Pascal: Nvidia's Newest Computing Platform" . Retrieved 13 September 2016.
  33. 1 2 Smith, Ryan (20 June 2016). "NVidia Announces PCI Express Tesla P100". Anandtech.com. Retrieved 21 June 2016.
  34. 1 2 Smith, Ryan (10 May 2017). "The Nvidia GPU Technology Conference 2017 Keynote Live Blog". Anandtech. Retrieved 10 May 2017.
  35. 1 2 Smith, Ryan (10 May 2017). "NVIDIA Volta Unveiled: GV100 GPU and Tesla V100 Accelerator Announced". Anandtech. Retrieved 10 May 2017.
  36. 1 2 Oh, Nate (20 June 2017). "NVIDIA Formally Announces V100: Available later this Year". Anandtech.com. Retrieved 20 June 2017.
  37. ↑ "NVIDIA TESLA T4 TENSOR CORE GPU". NVIDIA. Retrieved 17 October 2018.
  38. ↑ "NVIDIA Tesla T4 Tensor Core Product Brief" (PDF). www.nvidia.com. Retrieved 10 July 2019.
  39. ↑ "NVIDIA TESLA A2 TENSOR CORE GPU".
  40. ↑ "NVIDIA TESLA A10 TENSOR CORE GPU".
  41. ↑ "NVIDIA TESLA A16 TENSOR CORE GPU".
  42. ↑ "NVIDIA TESLA A30 TENSOR CORE GPU".
  43. ↑ "NVIDIA TESLA A40 TENSOR CORE GPU".
  44. ↑ "NVIDIA TESLA A100 TENSOR CORE GPU". NVIDIA. Retrieved 14 January 2021.
  45. ↑ "NVIDIA Tesla A100 Tensor Core Product Brief" (PDF). www.nvidia.com. Retrieved 22 September 2020.
  46. ↑ Smith, Ryan (14 May 2020). "NVIDIA Ampere Unleashed: NVIDIA Announces New GPU Architecture, A100 GPU, and Accelerator". AnandTech.
  47. ↑ "NVIDIA H100 Tensor Core GPU". NVIDIA. Retrieved 15 April 2024.
  48. ↑ Mujtaba, Hassan (22 March 2022). "NVIDIA Hopper GH100 GPU Unveiled: The World's First & Fastest 4nm Data Center Chip, Up To 4000 TFLOPs Compute, HBM3 3 TB/s Memory". Wccftech. Retrieved 15 April 2024.
  49. ↑ "NVIDIA H100 PCIe 80 GB Specs". TechPowerUp. 21 March 2023. Retrieved 15 April 2024.
  50. ↑ "NVIDIA L40 GPU for Data Center". NVIDIA. 18 May 2023. Retrieved 15 April 2024.
  51. ↑ "NVIDIA L40 Specs". TechPowerUp. 13 October 2022. Retrieved 15 April 2024.
  52. ↑ "NVIDIA L4 Tensor Core GPU". NVIDIA. Retrieved 15 April 2024.
  53. ↑ "NVIDIA ADA GPU Architecture" (PDF). nvidia.com. Retrieved 15 April 2024.
  54. ↑ "NVIDIA and Google Cloud Deliver Powerful New Generative AI Platform, Built on the New L4 GPU and Vertex AI". NVIDIA Corporation. 21 March 2023. Retrieved 15 April 2024.
  55. ↑ "NVIDIA L4 Specs". TechPowerUp. 21 March 2023. Retrieved 15 April 2024.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.