Nvidia Tesla used to be the name of Nvidia's line of products 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.

Nvidia Tesla
Nvidia Tesla GPU
ManufacturerNvidia
IntroducedMay 2, 2007;
16 years ago
 (2007-05-02)
DiscontinuedThe brand Tesla discontinued on May 2020; 3 years ago (2020-05), nowadays it's branded as Nvidia Data Center GPUs
TypeGeneral purpose graphics cards

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 A100 GPU.[3]

Overview edit

 
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]

As part of Project Denver, Nvidia intends to embed ARMv8 processor cores in its GPUs.[6] This will be a 64-bit follow-up to the 32-bit Tegra chips.

The Tesla P100 uses TSMC's 16 nanometer FinFET semiconductor manufacturing process, which is more advanced than the 28-nanometer process previously used by AMD and Nvidia GPUs between 2012 and 2016. The P100 also uses Samsung's HBM2 memory.[7]

Applications edit

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.[8]

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.[9]

Specifications edit

Model Micro-
architecture
Launch Chips Core clock
(MHz)
Shaders Memory Processing power (GFLOPS)[a] CUDA
compute
capability[b]
TDP
(watts)
Notes, form factor
Cuda cores
(total)
Base clock (MHz) Max boost
clock (MHz)[c]
Bus type Bus width
(bit)
Size
(GB)
Clock
(MT/s)
Bandwidth
(GB/s)
Half precision
Tensor Core FP32 Accumulate
Single precision
(MAD or FMA)
Double precision
(FMA)
Units MHz MHz W
C870 GPU Computing Module[d] Tesla May 2, 2007 1× G80 600 128 1,350 GDDR3 384 1.5 1,600 76.8 No 345.6 No 1.0 170.9 Internal PCIe GPU (full-height, dual-slot)
D870 Deskside Computer[d] May 2, 2007 2× G80 600 256 1,350 GDDR3 2× 384 2× 1.5 1,600 2× 76.8 No 691.2 No 1.0 520 Deskside or 3U rack-mount external GPUs
S870 GPU Computing Server[d] May 2, 2007 4× G80 600 512 1,350 GDDR3 4× 384 4× 1.5 1,600 4× 76.8 No 1382.4 No 1.0 1U rack-mount external GPUs, connect via 2× PCIe (×16)
C1060 GPU Computing Module[e] April 9, 2009 1× GT200 602 240 1,296[11] GDDR3 512 4 1,600 102.4 No 622.08 77.76 1.3 187.8 Internal PCIe GPU (full-height, dual-slot)
S1070 GPU Computing Server "400 configuration"[e] June 1, 2008 4× GT200 602 960 1296 GDDR3 4× 512 4× 4 1,538.4 4× 98.5 No 2,488.3 311.0 1.3 800 1U rack-mount external GPUs, connect via 2× PCIe (×8 or ×16)
S1070 GPU Computing Server "500 configuration"[e] 1,440 No 2,764.8 345.6
S1075 GPU Computing Server[e][12] June 1, 2008 4× GT200 602 960 1,440 GDDR3 4× 512 4× 4 1,538.4 4× 98.5 No 2,764.8 345.6 1.3 1U rack-mount external GPUs, connect via 1× PCIe (×8 or ×16)
Quadro Plex 2200 D2 Visual Computing System[f] July 25, 2008 2× GT200GL 648 480 1,296 GDDR3 2× 512 2× 4 1,600 2× 102.4 No 1,244.2 155.5 1.3 Deskside or 3U rack-mount external GPUs with 4 dual-link DVI outputs
Quadro Plex 2200 S4 Visual Computing System[f] July 25, 2008 4× GT200GL 648 960 1,296 GDDR3 4× 512 4× 4 1,600 4× 102.4 No 2,488.3 311.0 1.3 1,200 1U rack-mount external GPUs, connect via 2× PCIe (×8 or ×16)
C2050 GPU Computing Module[13] Fermi July 25, 2011 1× GF100 575 448 1,150 GDDR5 384 3[g] 3000 144 No 1,030.4 515.2 2.0 247 Internal PCIe GPU (full-height, dual-slot)
M2050 GPU Computing Module[14] July 25, 2011 3,092 148.4 No 225
C2070 GPU Computing Module[13] July 25, 2011 1× GF100 575 448 1,150 GDDR5 384 6[g] 3,000 144 No 1,030.4 515.2 2.0 247 Internal PCIe GPU (full-height, dual-slot)
C2075 GPU Computing Module[15] July 25, 2011 3,000 144 No 225
M2070/M2070Q GPU Computing Module[16] July 25, 2011 3,132 150.336 No 225
M2090 GPU Computing Module[17] July 25, 2011 1× GF110 650 512 1,300 GDDR5 384 6[g] 3700 177.6 No 1,331.2 665.6 2.0 225 Internal PCIe GPU (full-height, dual-slot)
S2050 GPU Computing Server July 25, 2011 4× GF100 575 1792 1150 GDDR5 4× 384 4× 3[g] 3 4× 148.4 No 4,121.6 2,060.8 2.0 900 1U rack-mount external GPUs, connect via 2× PCIe (×8 or ×16)
S2070 GPU Computing Server 4× 6[g] No
K10 GPU accelerator[18] Kepler May 1, 2012 2× GK104 3,072 745 ? GDDR5 2× 256 2× 4 5,000 2× 160 No 4,577 190.7 3.0 225 Internal PCIe GPU (full-height, dual-slot)
K20 GPU accelerator[19][20] November 12, 2012 1× GK110 2,496 706 758 GDDR5 320 5 5,200 208 No 3,524 1,175 3.5 225 Internal PCIe GPU (full-height, dual-slot)
K20X GPU accelerator[21] November 12, 2012 1× GK110 2,688 732 ? GDDR5 384 6 5,200 250 No 3,935 1,312 3.5 235 Internal PCIe GPU (full-height, dual-slot)
K40 GPU accelerator[22] October 8, 2013 1× GK110B 2,880 745 875 GDDR5 384 12[g] 6,000 288 No 4,291–5,040 1,430–1,680 3.5 235 Internal PCIe GPU (full-height, dual-slot)
K80 GPU accelerator[23] November 17, 2014 2× GK210 4,992 560 875 GDDR5 2× 384 2× 12 5,000 2× 240 No 5,591–8,736 1,864–2,912 3.7 300 Internal PCIe GPU (full-height, dual-slot)
M4 GPU accelerator[24][25] Maxwell November 10, 2015 1× GM206 1,024 872 1,072 GDDR5 128 4 5,500 88 No 1,786–2,195 55.81–68.61 5.2 50–75 Internal PCIe GPU (half-height, single-slot)
M6 GPU accelerator[26] August 30, 2015 1× GM204-995-A1 1536 722 1,051 GDDR5 256 8 4,600 147.2 No 2,218–3,229 69.3–100.9 5.2 75–100 Internal MXM GPU
M10 GPU accelerator[27] 4× GM107 2,560 1,033 ? GDDR5 4× 128 4× 8 5,188 4× 83 No 5,289 165.3 5.2 225 Internal PCIe GPU (full-height, dual-slot)
M40 GPU accelerator[25][28] November 10, 2015 1× GM200 3,072 948 1,114 GDDR5 384 12 or 24 6,000 288 No 5,825–6,844 182.0–213.9 5.2 250 Internal PCIe GPU (full-height, dual-slot)
M60 GPU accelerator[29] August 30, 2015 2× GM204-895-A1 4,096 899 1,178 GDDR5 2× 256 2× 8 5,000 2× 160 No 7,365–9,650 230.1–301.6 5.2 225–300 Internal PCIe GPU (full-height, dual-slot)
P4 GPU accelerator[30] Pascal September 13, 2016 1× GP104 2,560 810 1,063 GDDR5 256 8 6,000 192.0 No 4,147–5,443 129.6–170.1 6.1 50-75 PCIe card
P6 GPU accelerator[31][32] March 24, 2017 1× GP104-995-A1 2,048 1,012 1,506 GDDR5 256 16 3,003 192.2 No 6,169 192.8 6.1 90 MXM card
P40 GPU accelerator[30] September 13, 2016 1× GP102 3,840 1,303 1,531 GDDR5 384 24 7,200 345.6 No 10,007–11,758 312.7–367.4 6.1 250 PCIe card
P100 GPU accelerator (mezzanine)[33][34] April 5, 2016 1× GP100-890-A1 3,584 1,328 1,480 HBM2 4,096 16 1,430 732 No 9,519–10,609 4,760–5,304 6.0 300 SXM card
P100 GPU accelerator (16 GB card)[35] June 20, 2016 1× GP100 1126 1303 No 8,071‒9,340 4,036‒4,670 250 PCIe card
P100 GPU accelerator (12 GB card)[35] June 20, 2016 3,072 12 549 No 8,071‒9,340 4,036‒4,670
V100 GPU accelerator (mezzanine)[36][37][38] Volta May 10, 2017 1× GV100-895-A1 5120 Un­known 1,455 HBM2 4,096 16 or 32 1,750 900 119,192 14,899 7,450 7.0 300 SXM card
V100 GPU accelerator (PCIe card)[36][37][38] June 21, 2017 1× GV100 Un­known 1,370 112,224 14,028 7,014 250 PCIe card
V100 GPU accelerator (PCIe FHHL card) March 27, 2018 1× GV100 937 1,290 16 1,620 829.44 105,680 13,210 6,605 250 PCIe FHHL card
T4 GPU accelerator (PCIe card)[39][40] Turing September 12, 2018 1× TU104-895-A1 2,560 585 1,590 GDDR6 256 16 5,000 320 64,800 8,100 Un­known 7.5 70 PCIe card
A2 GPU accelerator (PCIe card)[41] Ampere November 10, 2021 1× GA107 1,280 1,440 1,770 GDDR6 128 16 6,252 200 18,124 4,531 140 8.6 40-60 PCIe card (half height, single-slot)
A10 GPU accelerator (PCIe card)[42] April 12, 2021 1× GA102-890-A1 9,216 885 1,695 GDDR6 384 24 6,252 600 124,960 31,240 976 8.6 150 PCIe card (single-slot)
A16 GPU accelerator (PCIe card)[43] April 12, 2021 4× GA107 4× 1,280 885 1,695 GDDR6 4× 128 4× 16 7,242 4× 200 4x 18,432 4× 4,608 1,084.8 8.6 250 PCIe card (dual-slot)
A30 GPU accelerator (PCIe card)[44] April 12, 2021 1× GA100 3,584 930 1,440 HBM2 3,072 24 1,215 933.1 165,120 10,320 5,161 8.0 165 PCIe card (dual-slot)
A40 GPU accelerator (PCIe card)[45] October 5, 2020 1× GA102 10,752 1,305 1,740 GDDR6 384 48 7,248 695.8 149,680 37,420 1,168 8.6 300 PCIe card (dual-slot)
A100 GPU accelerator (PCIe card)[46][47] May 14, 2020[48] 1× GA100-883AA-A1 6,912 765 1410 HBM2 5,120 40 or 80 1,215 1,555 312,000 19,500 9,700 8.0 250 PCIe card (dual-slot)
H100 GPU accelerator (PCIe card)[49] Hopper March 22, 2022[50] 1× GH100[51] 14,592 1,065 1,755 CUDA 1620 TC HBM2e 5120 80 1,000 2,039 756,449 51,200 25,600 9.0 350 PCIe card (dual-slot)
H100 GPU accelerator (SXM card) 16,896 1,065 1,980 CUDA 1,830 TC HBM3 5,120 80 1,500 3,352 989,430 66,900 33,500 9.0 700 SXM card
L40 GPU accelerator[52] Ada Lovelace October 13, 2022 1× AD102[53] 18,176 735 2,490 GDDR6 384 48 2,250 864 362,066 90,516 1,414 8.9 300 PCIe card (dual-slot)
L4 GPU accelerator[54][55] March 21, 2023[56] 1x AD104[57] 7,424 795 2,040 GDDR6 192 24 1,563 300 121,000 30,300 490 8.9 72 HHHL single slot PCIe card
Model Micro-
architecture
Launch Chips Core clock
(MHz)
Shaders Memory Processing power (GFLOPS)[a] CUDA
compute
capability
TDP
(watts)
Notes, form factor
Cuda cores
(total)
Base clock (MHz) Max boost
clock (MHz)[c]
Bus type Bus width
(bit)
Size
(GB)
Clock
(MT/s)
Bandwidth
(GB/s)
Half precision
Tensor Core FP32 Accumulate
Single precision
(MAD or FMA)
Double precision
(FMA)

Notes

  1. ^ a b 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. ^ a b 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.[10]
  4. ^ a b c Specifications not specified by Nvidia assumed to be based on the GeForce 8800 GTX
  5. ^ a b c d Specifications not specified by Nvidia assumed to be based on the GeForce GTX 280
  6. ^ a b Specifications not specified by Nvidia assumed to be based on the Quadro FX 5800
  7. ^ a b c d e f 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 edit

References edit

  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. ^ "Nvidia to Integrate ARM Processors in Tesla". 1 November 2012.
  7. ^ Walton, Mark (6 April 2016). "Nvidia unveils first Pascal graphics card, the monstrous Tesla P100". Ars Technica. Retrieved 19 June 2019.
  8. ^ Tesla Technical Brief (PDF)
  9. ^ "Nvidia chases defense, intelligence ISVs with GPUs". www.theregister.com. Retrieved 8 July 2020.
  10. ^ "Nvidia GPU Boost For Tesla" (PDF). January 2014. Retrieved 7 December 2015.
  11. ^ "Tesla C1060 Computing Processor Board" (PDF). Nvidia.com. Retrieved 11 December 2015.
  12. ^ "Difference between Tesla S1070 and S1075". 31 October 2008. Retrieved 29 January 2017. S1075 has one interface card
  13. ^ a b "Tesla C2050 and Tesla C2070 Computing Processor" (PDF). Nvidia.com. Retrieved 11 December 2015.
  14. ^ "Tesla M2050 and Tesla M2070/M2070Q Dual-Slot Computing Processor Modules" (PDF). Nvidia.com. Retrieved 11 December 2015.
  15. ^ "Tesla C2075 Computing Processor Board" (PDF). Nvidia.com. Retrieved 11 December 2015.
  16. ^ Hand, Randall (23 August 2010). "NVidia Tesla M2050 & M2070/M2070Q Specs OnlineVizWorld.com". VizWorld.com. Retrieved 11 December 2015.
  17. ^ "Tesla M2090 Dual-Slot Computing Processor Module" (PDF). Nvidia.com. Retrieved 11 December 2015.
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  19. ^ "Tesla K20 GPU active accelerator" (PDF). Nvidia.com. Retrieved 11 December 2015.
  20. ^ "Tesla K20 GPU accelerator" (PDF). Nvidia.com. Retrieved 11 December 2015.
  21. ^ "Tesla K20X GPU accelerator" (PDF). Nvidia.com. Retrieved 11 December 2015.
  22. ^ "Tesla K40 GPU accelerator" (PDF). Nvidia.com. Retrieved 11 December 2015.
  23. ^ "Tesla K80 GPU accelerator" (PDF). Images.nvidia.com. Retrieved 11 December 2015.
  24. ^ "Nvidia Announces Tesla M40 & M4 Server Cards - Data Center Machine Learning". Anandtech.com. Retrieved 11 December 2015.
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  27. ^ "Tesla M10" (PDF). Images.nvidia.com. Retrieved 29 October 2016.
  28. ^ "Tesla M40" (PDF). Images.nvidia.com. Retrieved 11 December 2015.
  29. ^ "Tesla M60" (PDF). Images.nvidia.com. Retrieved 27 May 2016.
  30. ^ a b Smith, Ryan (13 September 2016). "Nvidia Announces Tesla P40 & Tesla P4 - Network Inference, Big & Small". Anandtech. Retrieved 13 September 2016.
  31. ^ "Tesla P6" (PDF). www.nvidia.com. Retrieved 7 March 2019.
  32. ^ "Tesla P6 Specs". www.techpowerup.com. Retrieved 7 March 2019.
  33. ^ Smith, Ryan (5 April 2016). "Nvidia Announces Tesla P100 Accelerator - Pascal GP100 for HPC". Anandtech.com. Anandtech.com. Retrieved 5 April 2016.
  34. ^ Harris, Mark. "Inside Pascal: Nvidia's Newest Computing Platform". Retrieved 13 September 2016.
  35. ^ a b Smith, Ryan (20 June 2016). "NVidia Announces PCI Express Tesla P100". Anandtech.com. Retrieved 21 June 2016.
  36. ^ a b Smith, Ryan (10 May 2017). "The Nvidia GPU Technology Conference 2017 Keynote Live Blog". Anandtech. Retrieved 10 May 2017.
  37. ^ a b Smith, Ryan (10 May 2017). "NVIDIA Volta Unveiled: GV100 GPU and Tesla V100 Accelerator Announced". Anandtech. Retrieved 10 May 2017.
  38. ^ a b Oh, Nate (20 June 2017). "NVIDIA Formally Announces V100: Available later this Year". Anandtech.com. Retrieved 20 June 2017.
  39. ^ "NVIDIA TESLA T4 TENSOR CORE GPU". NVIDIA. Retrieved 17 October 2018.
  40. ^ "NVIDIA Tesla T4 Tensor Core Product Brief" (PDF). www.nvidia.com. Retrieved 10 July 2019.
  41. ^ "NVIDIA TESLA A2 TENSOR CORE GPU".
  42. ^ "NVIDIA TESLA A10 TENSOR CORE GPU".
  43. ^ "NVIDIA TESLA A16 TENSOR CORE GPU".
  44. ^ "NVIDIA TESLA A30 TENSOR CORE GPU".
  45. ^ "NVIDIA TESLA A40 TENSOR CORE GPU".
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  49. ^ https://www.nvidia.com/en-us/data-center/h100/
  50. ^ https://wccftech.com/nvidia-hopper-gh100-gpu-official-5nm-process-worlds-fastest-hpc-chip-80-billion-transistors-hbm3-memory/
  51. ^ https://www.techpowerup.com/gpu-specs/h100-pcie.c3899
  52. ^ https://www.nvidia.com/en-us/data-center/l40/
  53. ^ https://www.techpowerup.com/gpu-specs/l40.c3959
  54. ^ https://www.nvidia.com/en-us/data-center/l4/
  55. ^ https://images.nvidia.com/aem-dam/Solutions/Data-Center/l4/nvidia-ada-gpu-architecture-whitepaper-v2.1.pdf
  56. ^ https://investor.nvidia.com/news/press-release-details/2023/NVIDIA-and-Google-Cloud-Deliver-Powerful-New-Generative-AI-Platform-Built-on-the-New-L4-GPU-and-Vertex-AI/default.aspx
  57. ^ https://www.techpowerup.com/gpu-specs/l4.c4091

External links edit