darknet_letters
Différences
Ci-dessous, les différences entre deux révisions de la page.
Les deux révisions précédentesRévision précédenteProchaine révision | Révision précédenteProchaine révisionLes deux révisions suivantes | ||
darknet_letters [2020/06/20 07:02] – [Quels matériels pour un apprentissage rapide ?] serge | darknet_letters [2020/06/20 07:05] – [Quels matériels pour un apprentissage rapide ?] serge | ||
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* **[[https:// | * **[[https:// | ||
- | * RTX 2060 (6 GB): if you want to explore deep learning in your spare time. | + | * RTX 2060 (6 GB): if you want to explore deep learning in your spare time. 360€ |
* RTX 2070 or 2080 (8 GB): if you are serious about deep learning, but your GPU budget is $600-800. Eight GB of VRAM can fit the majority of models. | * RTX 2070 or 2080 (8 GB): if you are serious about deep learning, but your GPU budget is $600-800. Eight GB of VRAM can fit the majority of models. | ||
* RTX 2080 Ti (11 GB): if you are serious about deep learning and your GPU budget is ~$1,200. The RTX 2080 Ti is ~40% faster than the RTX 2080. | * RTX 2080 Ti (11 GB): if you are serious about deep learning and your GPU budget is ~$1,200. The RTX 2080 Ti is ~40% faster than the RTX 2080. | ||
- | * Titan RTX and Quadro RTX 6000 (24 GB): if you are working on SOTA models extensively, | + | * Titan RTX and Quadro RTX 6000 (24 GB): if you are working on SOTA models extensively, |
- | * Quadro RTX 8000 (48 GB): you are investing in the future and might even be lucky enough to research SOTA deep learning in 2020. | + | * Quadro RTX 8000 (48 GB): you are investing in the future and might even be lucky enough to research SOTA deep learning in 2020. 5500€ |
darknet_letters.txt · Dernière modification : 2020/12/27 15:11 de serge