Chardan Capital Reaffirms Buy Rating for QumulusAI (NASDAQ:QMLS)

QumulusAI (NASDAQ:QMLS)‘s stock had its “buy” rating reiterated by research analysts at Chardan Capital in a report released on Wednesday, Benzinga reports. They presently have a $23.00 price target on the stock. Chardan Capital’s price target indicates a potential upside of 254.94% from the company’s previous close.

A number of other research analysts have also weighed in on the stock. Zacks Research raised shares of QumulusAI to a “hold” rating in a research report on Friday, August 28th. Wall Street Zen raised shares of QumulusAI to a “hold” rating in a research report on Saturday, July 25th. One equities research analyst has rated the stock with a Buy rating and one has given a Hold rating to the company. Based on data from MarketBeat.com, QumulusAI currently has a consensus rating of “Moderate Buy” and an average target price of $23.00.

QumulusAI Trading Down 0.8%

Shares of QMLS stock traded down $0.05 on Wednesday, hitting $6.48. The company’s stock had a trading volume of 470,650 shares, compared to its average volume of 509,653. QumulusAI has a fifty-two week low of $5.06 and a fifty-two week high of $38.00. The firm’s fifty day moving average price is $6.47.

QumulusAI (NASDAQ:QMLS) last released its earnings results on Tuesday, August 25th. The company reported ($0.72) earnings per share for the quarter. The business 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.

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.