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Run massive models on crappy machines (Ep. 279)

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Manage episode 475113508 series 2600992
Content provided by Francesco Gadaleta. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Francesco Gadaleta or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

AI shouldn’t be limited to those with access to expensive hardware. This episode explores how to break down barriers by running massive AI models on "crappy machines"—affordable, low-spec devices. Clever techniques like quantization, pruning, model distillation might not be enough.

With edge offloading, we can make state-of-the-art AI accessible to hobbyists, researchers, and innovators everywhere.

By democratizing AI, we can empower individuals and small teams to experiment, create, and solve problems without needing deep pockets or enterprise-grade resources.

AI for everyone, on everything. Let’s make it happen.

✨ Connect with us!

📩 Newsletter: https://datascienceathome.substack.com

🎙 Podcast: Available on Spotify, Apple Podcasts, and more.

🐦 Twitter: @DataScienceAtHome

📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/

Instagram: https://www.instagram.com/datascienceathome/

Facebook: https://www.facebook.com/datascienceAH

LinkedIn: https://www.linkedin.com/company/data-science-at-home-podcast

Discord Channel: https://discord.gg/4UNKGf3

NEW TO DATA SCIENCE AT HOME?

Welcome! Data Science at Home explores the latest in AI, data science, and machine learning. Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions. Learn more at https://datascienceathome.com

SEND US MAIL!

We love hearing from you! Send us mail at:

[email protected]

Don’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!

  continue reading

283 episodes

Artwork
iconShare
 
Manage episode 475113508 series 2600992
Content provided by Francesco Gadaleta. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Francesco Gadaleta or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

AI shouldn’t be limited to those with access to expensive hardware. This episode explores how to break down barriers by running massive AI models on "crappy machines"—affordable, low-spec devices. Clever techniques like quantization, pruning, model distillation might not be enough.

With edge offloading, we can make state-of-the-art AI accessible to hobbyists, researchers, and innovators everywhere.

By democratizing AI, we can empower individuals and small teams to experiment, create, and solve problems without needing deep pockets or enterprise-grade resources.

AI for everyone, on everything. Let’s make it happen.

✨ Connect with us!

📩 Newsletter: https://datascienceathome.substack.com

🎙 Podcast: Available on Spotify, Apple Podcasts, and more.

🐦 Twitter: @DataScienceAtHome

📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/

Instagram: https://www.instagram.com/datascienceathome/

Facebook: https://www.facebook.com/datascienceAH

LinkedIn: https://www.linkedin.com/company/data-science-at-home-podcast

Discord Channel: https://discord.gg/4UNKGf3

NEW TO DATA SCIENCE AT HOME?

Welcome! Data Science at Home explores the latest in AI, data science, and machine learning. Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions. Learn more at https://datascienceathome.com

SEND US MAIL!

We love hearing from you! Send us mail at:

[email protected]

Don’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!

  continue reading

283 episodes

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