面向深度学习用户的Nvidia各版本GPU参数汇总整理

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做深度学习离不开算力,就像到达诗和远方的旅途离不开交通工具。本文面向深度学习用户整理市面上常用的各个版本GPU的关键参数,进行汇总整理,方便大家随时查阅。如果数据有误,或有新GPU问世,或者需要补充的参数维度,各位看官可以及时通过联系方式来找我更新数据。

注:仅统计Memory在 24GB及以上、发售时间在2017年及以后、算力性能满足深度学习计算要求(Cuda算力性能>=7.0)的GPU。若表中单元格为空,则为暂无数据或数据待补充。博主将不定期移除上市时间最久、已停产过时、市面上已大量退役的GPU数据信息项。

(当前数据整理截止为2025年8月)

Tesla系列

版本型号MemoryCUDA CoreCUDA性能FP8/INT8
(F)OPs
FP16/BF16
半精度FLOPs
FP32
单精度FLOPs
FP64
双精度FLOPs
总线位宽总线带宽网络最大功率发布时间原价¥
B系B10096 GB *2
HBM3e
16896*23.5 P1.8P0.9P30T4096位*28 TB/s700w2024
B系B200180GB
HBM3e
4.5P2.25P1.1P40T4096位*28 TB/s400Gbps1000w2024
B系B3002.3TB
HBM
72 P36P600T10T14.4TB/s1.6TB/s14kw2025
B系B302025
H系H10094GB/80GB
HBM2e/HBM3
14592
16896
9.03341T
3958T
1671T
1979T
60T
67T
30T
34T
5120位2039GB/s350w
700w
2022.0326.4w
H系H200141GB
HBM3e
3341T
3958T
1671T
1979T
60T
67T
30T
34T
4.8TB/s600w
700w
H系H80080GB
HBM2e/HBM3
184324P2P60T34T2TB/s(HBM2e)
3.9TB/s(HBM3)
350w
700w
2023.03
H系H2096 GB
HBM3
296T148T44T1T4.0 TB/s
L系L4048GB
GDDR6
181768.9362.066T90.516T1.414T384位864GB/s300w2022.10
L系L2048GB
HBM3
10240239T119.5T59.8TNA384位864 GB/s
L系L424GB
GDDR6
74248.9121T30.3T0.49T192位300GB/s72w2023.03
A系A10040GB/80GB
HBM2
69128.0624T312T19.5T9.7T5120位1555GB/s400w2020.05
A系A80040GB/80GB
HBM2
69121248T312T19.5T9.7T1.6 TB/s400w2022.118.7w
A系A4048GB
GDDR6
107528.6149.68T37.42T1.168T384位695.8GB/s300w2020.10
A系A3024GB
HBM2
35848.0165.12T10.32T5.161T3072位933.1GB/s165w2021.04
A系A1024GB
GDDR6
92168.6124.96T31.24T0.976T384位600GB/s150w2021.04
A系A164*16GB
GDDR6
4*12808.64*18.432T4*4.608T1.0848T4*128位4*200GB/s250w2021.04
V系V10032GB
HBM2
51207.0119.192T
112.224T
105.680T
14.899T
14.028T
13.210T
7.450T
7.014T
6.605T
4096位900GB/s
829.44GB/s
250w2017.05

GeForce系列

版本型号MemoryCUDA CoreCUDA性能FP8/INT8
T(F)OPs
FP16/BF16
半精度TFLOPs
FP32
单精度TFLOPs
FP64
双精度TFLOPs
显存位宽总线带宽最大功率发布时间原价¥
50系RTX 509032GB
GDDR7
21760
12.8
512位1792GB/s575w$1999
50系RTX 5090D32 GB GDDR72176012.8512位1792GB/s575w16499
50系RTX5090DD24GB
GDDR7
14080384位500w?2025
40系RTX 409024GB
GDDR6X
163848.982.5882.581.290384位1008GB/s450w2022
10.12
12999
40系RTX 4090D24GB145928.973.5473.541.149384位1008GB/s425w2023
12.28
11999
30系RTX 3090 Ti24GB107528.633.54
39.99
33.54
39.99
0.524
0.625
384位1008GB/s450w2022
03.29
14999
30系RTX 309024GB104968.629.38
35.68
29.28
35.58
0.459
0.558
384位935.8GB/s350w2020
09.02
11999

Quadro系列

型号
MemoryCUDA CoreCUDA性能FP8/INT8
T(F)OPs
FP16/BF16
半精度TFLOPs
FP32
单精度TFLOPs
FP64
双精度TFLOPs
显存位宽总线带宽最大功率发布时间原价¥
RTX 600048GB
GDDR6
181768.991.1384位960GB/s300w
RTX 500032GB
GDDR6
250w
RTX 450024GB
GDDR6
210w
RTX A600048GB
GDDR6
8.6300w
RTX A500024GB
GDDR6
8.6230w
Quadro RTX 800048GB
GDDR6
7.5
Quadro RTX 600024GB
GDDR6
7.5

参考来源

  1. https://www.nvidia.cn/geforce/graphics-cards/40-series/
  2. https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
  3. https://www.nvidia.cn/geforce/graphics-cards/30-series/
  4. https://www.nvidia.cn/geforce/graphics-cards/compare/
  5. https://developer.nvidia.com/cuda-gpus
  6. https://zh.wikipedia.org/wiki/NVIDIA_Tesla
  7. https://www.bilibili.com/read/cv33922816/
  8. https://zh.wikipedia.org/wiki/NVIDIA_GeForce_40%E7%B3%BB%E5%88%97
  9. https://zh.wikipedia.org/wiki/NVIDIA_GeForce_30%E7%B3%BB%E5%88%97
  10. https://en.wikipedia.org/wiki/GeForce_40_series
  11. https://www.nvidia.com/en-us/design-visualization/rtx-6000/
  12. https://www.nvidia.com/en-us/design-visualization/desktop-graphics/
  13. https://resources.nvidia.com/en-us-design-viz-stories-ep/l40-linecard?lx=CCKW39&&search=professional%20graphics
  14. https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/productspage/quadro/quadro-desktop/quadro-volta-gv100-data-sheet-us-nvidia-704619-r3-web.pdf
  15. https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/quadro-product-literature/quadro-rtx-5000-data-sheet-us-nvidia-704120-r4-web.pdf
  16. https://www.nvidia.cn/design-visualization/rtx-5000/
  17. https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/quadro-product-literature/quadro-rtx-6000-us-nvidia-704093-r4-web.pdf
  18. https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/quadro-product-literature/quadro-rtx-8000-us-nvidia-946977-r1-web.pdf
  19. https://viperatech.com/shop/nvidia-hgx-h20/
  20. https://www.nvidia.com/en-us/data-center/h200/
  21. https://www.nvidia.com/en-us/data-center/h100/
  22. https://www.nvidia.cn/geforce/graphics-cards/50-series/rtx-5090-d/
  23. https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/
  24. https://www.nvidia.com/en-us/data-center/hgx/
  25. https://blog.csdn.net/Ai17316391579/article/details/132627201
  26. https://www.jb51.net/hardware/cpu/956950.html
  27. https://resources.nvidia.com/en-us-blackwell-architecture/datasheet
  28. https://www.techpowerup.com/gpu-specs/b100.c4275
  29. https://www.nvidia.com/en-us/data-center/a100/
  30. https://resources.nvidia.com/en-us-dgx-systems/dgx-b200-datasheet
  31. https://viperatech.com/shop/nvidia-b100-blackwell-ai-gpu/
  32. https://viperatech.com/shop/nvidia-dgx-b200/
  33. https://www.anandtech.com/show/21310/nvidia-blackwell-architecture-and-b200b100-accelerators-announced-going-bigger-with-smaller-data
  34. https://www.nvidia.com/en-us/data-center/dgx-b300/
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