- Topic
17k Popularity
17k Popularity
139k Popularity
3k Popularity
18k Popularity
- Pin
- 🎉 Hey Gate Square friends! Non-stop perks and endless excitement—our hottest posting reward events are ongoing now! The more you post, the more you win. Don’t miss your exclusive goodies! 🚀
🆘 #Gate 2025 Semi-Year Community Gala# | Square Content Creator TOP 10
Only 1 day left! Your favorite creator is one vote away from TOP 10. Interact on Square to earn Votes—boost them and enter the prize draw. Prizes: iPhone 16 Pro Max, Golden Bull sculpture, Futures Vouchers!
Details 👉 https://www.gate.com/activities/community-vote
1️⃣ #Show My Alpha Points# | Share your Alpha points & gains
Post your - 🚀 ETH jumped to $4,600 this morning, up 8.69% in 24h!
Just shy of the $4,891 ATH—think it breaks through?
📍 Follow Gate_Square, vote and drop your reason.
🎁 4 winners split $100 Futures Voucher! - 💙 Gate Square #Gate Blue Challenge# 💙
Show your limitless creativity with Gate Blue!
📅 Event Period
August 11 – 20, 2025
🎯 How to Participate
1. Post your original creation (image / video / hand-drawn art / digital work, etc.) on Gate Square, incorporating Gate’s brand blue or the Gate logo.
2. Include the hashtag #Gate Blue Challenge# in your post title or content.
3. Add a short blessing or message for Gate in your content (e.g., “Wishing Gate Exchange continued success — may the blue shine forever!”).
4. Submissions must be original and comply with community guidelines. Plagiarism or re
MiniMax Open Source's first inference model: Competing with DeepSeek, the Computing Power cost is only about $530,000.
Gate News bot message, MiniMax announced on June 17 that it will release important updates for five consecutive days. Today's first release is the Open Source first inference model MiniMax-M1.
According to the official report, the MiniMax-M1 has benchmarked alongside open source models such as DeepSeek-R1 and Qwen3, approaching the most advanced models overseas.
The official blog also mentioned that based on two major technological innovations, the MiniMax-M1 training process was efficient "beyond expectations," completing the reinforcement learning training phase in just 3 weeks using 512 H800 GPUs, with a computing power rental cost of only $534,700. This is an order of magnitude less than the initial expectations.
Source: Jinshi