QumulusAI’s (QMLS) Buy Rating Reaffirmed at Chardan Capital

QumulusAI (NASDAQ:QMLS)‘s stock had its “buy” rating restated by equities research analysts at Chardan Capital in a research note issued on Wednesday, Benzinga reports. They currently have a $23.00 target price on the stock. Chardan Capital’s price target suggests a potential upside of 252.44% from the stock’s current price.

QMLS has been the subject of several other reports. Wall Street Zen raised shares of QumulusAI to a “hold” rating in a research note on Saturday, July 25th. Zacks Research raised shares of QumulusAI to a “hold” rating in a research note on Friday, August 28th. One research analyst has rated the stock with a Buy rating and one has given a Hold rating to the stock. Based on data from MarketBeat, the company presently has a consensus rating of “Moderate Buy” and a consensus price target of $23.00.

QumulusAI Stock Performance

Shares of NASDAQ QMLS traded down $0.00 during mid-day trading on Wednesday, reaching $6.53. 198,308 shares of the company were exchanged, compared to its average volume of 503,647. QumulusAI has a 52 week low of $5.06 and a 52 week high of $38.00. The stock has a 50 day moving average of $6.52.

QumulusAI (NASDAQ:QMLS) last posted its quarterly earnings data on Tuesday, August 25th. The company reported ($0.72) earnings per share (EPS) for the quarter. The firm had revenue of $6.71 million for the quarter. As a group, analysts forecast that QumulusAI will post -2.71 EPS for the current fiscal year.

QumulusAI Company Profile

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.