ๆญฃๅœจๅŠ ่ฝฝ่ง†้ข‘...

่ง†้ข‘ๅŠ ่ฝฝๅคฑ่ดฅ

๐ˆ๐ง๐ญ๐ซ๐จ๐๐ฎ๐œ๐ข๐ง๐  ๐ญ๐ก๐ž ๐‹๐š๐ญ๐ž๐ฌ๐ญ ๐•๐ž๐ซ๐ฌ๐ข๐จ๐ง ๐จ๐Ÿ ๐€๐ˆ๐ฑ๐๐ฅ๐จ๐œ๐ค AIxBlock is an on-premise AI platform that enables you to productize AI models using unused computing resources while ensuring complete privacy. ๐Š๐ž๐ฒ ๐…๐ž๐š๐ญ๐ฎ๐ซ๐ž๐ฌ: 1๏ธโƒฃ ๐’๐ž๐ฅ๐Ÿ-๐‡๐จ๐ฌ๐ญ๐ข๐ง๐  โ˜€๏ธ Deploy in minutes. โ˜€๏ธ 100% privacy control. โ˜€๏ธ No long-term commitments or upfront costs. 2๏ธโƒฃ ๐‚๐ฎ๐ฌ๐ญ๐จ๐ฆ๐ข๐ณ๐ž๐ ๐–๐จ๐ซ๐ค๐Ÿ๐ฅ๐จ๐ฐ๐ฌ...

114,768 ๆฌก่ง‚็œ‹ โ€ข 1 ๅนดๅ‰ โ€ขvia X (Twitter)

9 ๆก่ฏ„่ฎบ

RA S EL POKEMON ็š„ๅคดๅƒ
RA S EL POKEMON1 ๅนดๅ‰

Nice project

Asraf ็š„ๅคดๅƒ
Asraf1 ๅนดๅ‰

It's really respectable that there's a team that's talking about a good future waiting for it. I'd like to be one of the best project #AlxBlock #solana @Nayonboss1 @Nipahasan12

jashohag33.base.eth ็š„ๅคดๅƒ
jashohag33.base.eth1 ๅนดๅ‰

Gooo

Jahangir Alom ็š„ๅคดๅƒ
Jahangir Alom1 ๅนดๅ‰

โค๏ธ

raphael araujo ็š„ๅคดๅƒ
raphael araujo1 ๅนดๅ‰

@AIxBlock

Sojib ็š„ๅคดๅƒ
Sojib1 ๅนดๅ‰

great

ู…ู‡ุฑุดุงุฏ ุญุณู†ูพูˆุฑ ็š„ๅคดๅƒ
ู…ู‡ุฑุดุงุฏ ุญุณู†ูพูˆุฑ1 ๅนดๅ‰

๐Ÿค”๐Ÿค”

sky.no1 ๊งIP๊ง‚ Reddio ็š„ๅคดๅƒ
sky.no1 ๊งIP๊ง‚ Reddio1 ๅนดๅ‰

The best project I've ever seen. It will take the future generation a lot further.Excellent project in a word

AIxBlock ็š„ๅคดๅƒ
AIxBlock1 ๅนดๅ‰

thank you so much for your support ๐Ÿ”ฅ

็›ธๅ…ณ่ง†้ข‘

๐ŸŒŸQuilibriumโ€™s AI Breakthrough: Encrypted Training on CPUs In her latest live stream ( - minute 14) Cassie unveiled a groundbreaking AI training method that allows models to be trained on encrypted data using CPUs while achieving performance comparable to Nvidiaโ€™s A100 GPU (blue line in the graph below). Traditionally, AI training requires expensive GPUs because matrix multiplicationsโ€”the core of deep learningโ€”are highly computational. Running these calculations on CPUs is painfully slow, often taking hours or days for even small models. The problem worsens when trying to train AI on encrypted data, as standard encryption methods add a massive computational burden. Quilibriumโ€™s breakthrough removes this bottleneck. Instead of relying on traditional matrix multiplication, their method uses a completely different mathematical approach, allowing AI models to be trained securely and efficiently without exposing the raw data. Cassie didnโ€™t reveal the exact technique, only hinting that itโ€™s inspired by existing AI research and will be detailed in a future open-source AGPL-licensed paper. The key advantage? AI can now be trained at GPU speeds on standard CPUs, making privacy-preserving machine learning far more accessible. This innovation has major implications. It slashes AI infrastructure costs, allowing organizations to train powerful models without investing in expensive hardware. It also enables private AI training on personal or corporate data without revealing sensitive information, a game-changer for industries like healthcare and finance. If Quilibriumโ€™s method delivers on its promise, it could reshape AI development, making privacy-first computing the new standard. $QUIL $wQUIL

Quilibrium Community

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