QumulusAI’s (QMLS) “Buy” Rating Reiterated at Chardan Capital

Chardan Capital reissued their buy rating on shares of QumulusAI (NASDAQ:QMLS) in a research note released on Wednesday morning,Benzinga reports. They currently have a $23.00 price target on the stock.

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

QumulusAI Stock Up 7.6%

NASDAQ:QMLS opened at $6.53 on Wednesday. The stock’s 50-day moving average price is $6.41. QumulusAI has a 52-week low of $5.06 and a 52-week high of $38.00.

QumulusAI (NASDAQ:QMLS) last released its quarterly 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 during the quarter. On average, research analysts predict that QumulusAI will post -2.71 earnings per share for the current 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.