Render (RENDER) Sentiment & Fear and Greed Index
As of July 5, 2026, Render's Ruma Fear & Greed Index is 12 (Extreme Fear), its social sentiment score is -31/100 (bearish), it holds 0.00% of crypto social mindshare. These signals are computed by Ruma from social posts across crypto Twitter/X and other sources, scored with large language models rather than keyword counts.
Updated continuously · Source: Ruma
Latest Render insights
Bittensor Subnet SN64, operating as Chutes AI ($TAO's SN64), has achieved the third position in global DePIN revenue. The project currently boasts an annualized revenue of $5.4 million. This places Chutes AI ahead of major platforms like Akash, Render, Filecoin, and Livepeer, with only Helium and Arweave ranking higher.
Render ($RENDER) has experienced a massive surge in daily trading volume, with 9.8 million RENDER traded. This represents one of the largest volume bars in months, indicating significant market activity beyond its 8% price increase.
RENDER Network ($RNDR) has experienced a significant surge in on-chain activity, reaching a 12-week high according to Santiment data. Concurrently, its price surpassed $2.25 for the first time in four months. This renewed investor interest is driven by increasing demand for AI infrastructure and decentralized GPU computing, indicating returning demand in the AI sector.
Frequently asked questions
What is Render's Fear & Greed Index?
Render's Ruma Fear & Greed Index is currently 12 out of 100, which is Extreme Fear. The index blends social sentiment, social interest, price momentum, volatility, and emotional intensity into a single 0–100 sentiment score, updated continuously.
Is Render bullish or bearish right now?
Render's social sentiment is currently bearish, with a sentiment score of -31/100 based on how bullish or bearish the crypto social conversation is. Sentiment reflects the mood of the market, not price direction or financial advice.
How does Ruma measure Render sentiment?
Ruma reads every relevant social post about Render across crypto Twitter/X and other sources and scores it with large language models — capturing bullish/bearish tone, emotion, and who is speaking (from retail to smart money) — rather than counting keywords.