QumulusAI (NASDAQ:QMLS) Stock Keeps “Buy” Rating at Chardan Capital

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

A number of other research firms have also recently issued reports on QMLS. Wall Street Zen raised QumulusAI to a “hold” rating in a research report on Saturday, July 25th. Zacks Research upgraded QumulusAI to a “hold” rating in a research note on Friday, August 28th. One equities research analyst has rated the stock with a Buy rating and one has issued a Hold rating to the company. According to data from MarketBeat.com, the stock presently has a consensus rating of “Moderate Buy” and an average target price of $23.00.

QumulusAI Stock Down 0.8%

QMLS opened at $6.48 on Wednesday. QumulusAI has a 12-month low of $5.06 and a 12-month high of $38.00. The company has a 50 day simple moving average of $6.47.

QumulusAI (NASDAQ:QMLS) last released 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 during the quarter. As a group, equities research analysts anticipate that QumulusAI will post -2.71 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.