Market Cap: $3.6315T -1.300%
Volume(24h): $133.5557B -36.440%
  • Market Cap: $3.6315T -1.300%
  • Volume(24h): $133.5557B -36.440%
  • Fear & Greed Index:
  • Market Cap: $3.6315T -1.300%
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Aventus
Aventus AVT
#1128
$
%(1d)

1.6 USD

10%(1d)

1.6 USDT

30%(1d)

Price performance

Low

High

$

$

Buy / Sell

All-time High

$11

Sep 13, 2017

-86.38%(1d)

All-time Low

$0.0021

Aug 29, 2020

71792.63%(1d)

Volume(24h)

$315.75K

{{decimal(volume_24h_change,false,2)}}%(1d)

Turnover rate

3.48%

Market Cap

$9.068M

FDV

$15.1M

Circulating supply

$6M

Total supply

$10M

Max supply

10M

Currency Calculator

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Community sentiment

26%

74%

Bullish

Bearish

Mario Nawfal’s Roundtable

Aug 03, 2025 at 08:45 am

READ MORE
UPDATE: ARTHUR HAYES () SOLD 2,373 $ETH ($8.32M), 7.76M $ENA ($4.62M), AND 38.86B $PEPE ($414.7K) IN THE LAST 6 HOURS. Source: JUST IN: ARTHUR HAYES (@CryptoHayes) SELLS $13.5M IN ETH, ENA, AND PEPE AMID NEW MARKET OUTLOOK Source: @MoreWaleed x.com/RoundtableSpac…

From Twitter

Atkn.sol

Aug 03, 2025 at 06:46 am

READ MORE
Every wallet Gets 1 $SOL Just drop your $SOL address, RT ♻️ & Like ❤️ 🔔 First 888

From Twitter

Cointelegraph

Aug 03, 2025 at 06:00 am

READ MORE
⚡HAYES: "No major econ is creating credit fast enough to boost GDP." He says $BTC could test $100K, $ETH $3K.

From Twitter

Altcoin Daily

Aug 03, 2025 at 05:53 am

READ MORE
What 1,000 $ADA Coins Will Be Worth in 2030... 👉 WATCH: https://youtu.be/wR7Znb12qxU

From Twitter

XRPcryptowolf

Aug 03, 2025 at 04:48 am

READ MORE
Why the fu*k is $XRP crashing so hard? 💀

From Twitter

More

Aventus News

Feb / 10

Feb / 08

Jan / 26

  • VideoLLaMA3: A Vision-Centric Framework for Multimodal Models with Any-Resolution Vision Tokenization and Differential Frame Pruner
    Jan 26, 2025 at 02:00 pm
    Advancements in multimodal intelligence depend on processing and understanding images and videos. Images can reveal static scenes by providing information regarding details such as objects, text, and spatial relationships. However, this comes at the cost of being extremely challenging. Video comprehension involves tracking changes over time, among other operations, while ensuring consistency across frames, requiring dynamic content management and temporal relationships. These tasks become tougher because the collection and annotation of video-text datasets are relatively difficult compared to the image-text dataset.

Oct / 23

Oct / 22

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