QumulusAI (NASDAQ:QMLS) Given Buy Rating at Chardan Capital

QumulusAI (NASDAQ:QMLS)‘s stock had its “buy” rating reiterated by research analysts at Chardan Capital in a note issued to investors on Wednesday, Benzinga reports. They presently have a $23.00 price objective on the stock. Chardan Capital’s target price would indicate a potential upside of 273.38% from the stock’s previous close.

Several other equities research analysts also recently commented on QMLS. Zacks Research upgraded QumulusAI to a “hold” rating in a report on Friday, August 28th. Wall Street Zen upgraded QumulusAI to a “hold” rating in a report on Saturday, July 25th. One investment analyst has rated the stock with a Buy rating and one has issued a Hold rating to the stock. Based on data from MarketBeat, QumulusAI currently has an average rating of “Moderate Buy” and an average price target of $23.00.

QumulusAI Price Performance

Shares of NASDAQ:QMLS traded down $0.37 on Wednesday, hitting $6.16. 232,099 shares of the company traded hands, compared to its average volume of 500,761. QumulusAI has a 52 week low of $5.06 and a 52 week high of $38.00. The business has a 50 day moving average price of $6.41.

QumulusAI (NASDAQ:QMLS) last released its quarterly earnings data on Tuesday, August 25th. The company reported ($0.72) EPS for the quarter. The company had revenue of $6.71 million during the quarter. On average, sell-side analysts anticipate that QumulusAI will post -2.93 earnings per share for the current fiscal year.

About QumulusAI

QumulusAI is a cloud infrastructure company specializing in rapid deployment of graphics processing unit (“GPU”)-powered solutions for artificial intelligence (“AI”) applications, serving a critical market that is often overlooked by large-scale cloud providers (“hyperscalers”), which operate massive, standardized computing infrastructures primarily serving the largest enterprises. Our platform delivers flexible, competitively priced, and customizable solutions for underserved small and mid-market customers—including machine learning teams, AI infrastructure startups, and research institutions—while also supporting the scale and complexity requirements of large enterprises, such as long-term deployments or supplemental on-demand compute capacity.