随便玩玩够用了 3060 12g行吗 https://timdettmers.com/2020/09/07/which-gpu-for-deep-learning/ 2060 12g
3060 12g
廉价的显存大就行 TL;DR advice
Best GPU overall: RTX 3080 and RTX 3090.
GPUs to avoid (as an individual): Any Tesla card; any Quadro card; any Founders Edition card; Titan RTX, Titan V, Titan XP.
Cost-efficient but expensive: RTX 3080.
Cost-efficient and cheaper:RTX 3070, RTX 2060 Super
I have little money: Buy used cards. Hierarchy: RTX 2070 ($400), RTX 2060 ($300), GTX 1070 ($220), GTX 1070 Ti ($230), GTX 1650 Super ($190), GTX 980 Ti (6GB $150).
I have almost no money: There are a lot of startups that promo their clouds: Use free cloud credits and switch companies accounts until you can afford a GPU.
I do Kaggle: RTX 3070.
I am a competitive computer vision, pretraining, or machine translation researcher: 4x RTX 3090. Wait until working builds with good cooling, and enough power are confirmed (I will update this blog post).
I am an NLP researcher: If you do not work on machine translation, language modeling, or pretraining of any kind, an RTX 3080 will be sufficient and cost-effective.
I started deep learning, and I am serious about it: Start with an RTX 3070. If you are still serious after 6-9 months, sell your RTX 3070 and buy 4x RTX 3080. Depending on what area you choose next (startup, Kaggle, research, applied deep learning), sell your GPUs, and buy something more appropriate after about three years (next-gen RTX 40s GPUs).
I want to try deep learning, but I am not serious about it: The RTX 2060 Super is excellent but may require a new power supply to be used. If your motherboard has a PCIe x16 slot and you have a power supply with around 300 W, a GTX 1050 Ti is a great option since it will not require any other computer components to work with your desktop computer.
GPU Cluster used for parallel models across less than 128 GPUs: If you are allowed to buy RTX GPUs for your cluster: 66% 8x RTX 3080 and 33% 8x RTX 3090 (only if sufficient cooling is guaranteed/confirmed). If cooling of RTX 3090s is not sufficient buy 33% RTX 6000 GPUs or 8x Tesla A100 instead. If you are not allowed to buy RTX GPUs, I would probably go with 8x A100 Supermicro nodes or 8x RTX 6000 nodes.
GPU Cluster used for parallel models across 128 GPUs: Think about 8x Tesla A100 setups. If you use more than 512 GPUs, you should think about getting a DGX A100 SuperPOD system that fits your scale. 不如租个云主机按需用
能体验顶级显卡的算力 同时成本也低 指不定你玩两天不想玩了呢... 哪怕买10系的卡都行，但是显存一定要够，12GB打底吧，还得扣去系统占有1到2GB 等40系 abcbuzhiming 发表于 2022-6-12 11:11
囧，一上来就玩顶级的吗？
炼丹来说这算入门卡 在国外或者能翻墙的话请买colab，啥时候玩不动了方便止损 当然是云服务。。。。这两天老板让我测试几家整体发展程度比我预计的要好很多
用谷歌自带colab玩玩吧 先白嫖colab(国内的话百度的ai studio)，做一些算法的复现足够了。真要做研究或者打比赛肯定是3080起步了。
— from OnePlus GM1917, Android 10 of S1 Next Goose v2.5.4 老黃不是禁止游戲卡使用煉丹技術了嗎 Realplayer 发表于 2022-6-12 15:25
老黃不是禁止游戲卡使用煉丹技術了嗎
炼丹是科学计算，老黄禁的是挖矿，而且最近二代算力锁的卡也都被破解了（12g3080那些） squarezty 发表于 2022-6-12 16:16
炼丹是科学计算，老黄禁的是挖矿，而且最近二代算力锁的卡也都被破解了（12g3080那些） ...
https://cloud.tencent.com/developer/news/8049禁止会社不禁个人，这是禁了个寂寞
偶尔还是要玩游戏的话，3050 8G，3060 12G，A4000 16G，3090 24G，按预算来。 显存12g往上，有钱请买8块h100 rtx2060 12g
显存小了跑不动
速度倒不是问题，一天算不出来那就两天呗
也可以租用云显卡
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