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

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

Other equities research analysts have also recently issued research reports about the company. Zacks Research raised QumulusAI to a “hold” rating in a research note on Friday, August 28th. Wall Street Zen upgraded shares of QumulusAI to a “hold” rating in a research note on Saturday, July 25th. One analyst has rated the stock with a Buy rating and one has assigned a Hold rating to the company’s stock. According to MarketBeat.com, the company has an average rating of “Moderate Buy” and a consensus target price of $23.00.

QumulusAI Stock Performance

QumulusAI stock traded down $0.05 during mid-day trading on Wednesday, hitting $6.48. 466,212 shares of the stock were exchanged, compared to its average volume of 509,581. QumulusAI has a 12 month low of $5.06 and a 12 month high of $38.00. The firm’s fifty day moving average price is $6.52.

QumulusAI (NASDAQ:QMLS) last released its earnings results on Tuesday, August 25th. The company reported ($0.72) EPS for the quarter. The company had revenue of $6.71 million during the quarter. As a group, equities research analysts predict 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.