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lafeoooooo

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1 ポイント·投稿者 lafeoooooo·昨年·0 コメント

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1 ポイント·投稿者 lafeoooooo·2 年前·0 コメント

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2 ポイント·投稿者 lafeoooooo·2 年前·0 コメント

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3 ポイント·投稿者 lafeoooooo·2 年前·0 コメント

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1 ポイント·投稿者 lafeoooooo·2 年前·0 コメント

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1 ポイント·投稿者 lafeoooooo·2 年前·0 コメント

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1 ポイント·投稿者 lafeoooooo·2 年前·0 コメント

コメント

lafeoooooo
·2 年前·議論
I use a chrome plugin to change the website font to OpenDyslexic. It's Interesting.

https://chromewebstore.google.com/detail/opendyslexic-for-ch...
lafeoooooo
·2 年前·議論
[dead]
lafeoooooo
·2 年前·議論
In organic search, Google's ranking algorithm takes into account various SEO factors like content quality and backlinks. For high-traffic search queries, thousands of websites compete fiercely for visibility, employing all sorts of optimization tactics. Wikipedia, while a treasure trove of information, doesn't actively engage in such SEO practices, putting it at a disadvantage compared to these highly optimized sites.
lafeoooooo
·2 年前·議論
Different scenarios have varying demands for GPU types. For tasks like model inference or basic operations, a CPU or even on-device solutions (mobile, web) might suffice.

When a GPU is necessary, common choices include T4, 3090, P10, V100, etc., selected based on factors like price, required computing power, and memory capacity.

Model training also has diverse needs based on the specific task. For basic, general-purpose vision tasks, 1 to 50 cards like the 3090 might be enough. However, cutting-edge areas like visual generation and LLMs often require A100s or A800s, scaling from 1 to even thousands of cards.