Technology

34912 readers

288 users here now

This is the official technology community of Lemmy.ml for all news related to creation and use of technology, and to facilitate civil, meaningful discussion around it.

Ask in DM before posting product reviews or ads. All such posts otherwise are subject to removal.

Rules:

1: All Lemmy rules apply

2: Do not post low effort posts

3: NEVER post naziped*gore stuff

4: Always post article URLs or their archived version URLs as sources, NOT screenshots. Help the blind users.

5: personal rants of Big Tech CEOs like Elon Musk are unwelcome (does not include posts about their companies affecting wide range of people)

6: no advertisement posts unless verified as legitimate and non-exploitative/non-consumerist

7: crypto related posts, unless essential, are disallowed

founded 5 years ago

MODERATORS

MinutePhrase@lemmy.ml

128

LLMs are surprisingly great at compressing images and audio, DeepMind researchers find (venturebeat.com)

submitted 1 year ago by floofloof@lemmy.ca to c/technology@lemmy.ml

19 comments fedilink hide all child comments

you are viewing a single comment's thread
view the rest of the comments

[–] falkerie71@sh.itjust.works 3 points 1 year ago

I don't know how this would apply to decompression models in actuality, but in general, deep learning is VRAM intensive only during the training process, that's because they train multiple batches of data at once for generalization, and all those batches of data need to be stored in ram.
But once the model is trained, the end user is only going to input data one by one, so VRAM usually is not an issue. There are also light weight models that are designed to be run on lower end hardware.