QumulusAI (NASDAQ:QMLS) Earns “Buy” Rating from Chardan Capital

QumulusAI (NASDAQ:QMLS – Get Free Report)‘s stock had its “buy” rating reiterated by analysts at Chardan Capital in a research note issued 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 252.22% from the company’s previous close.

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

View Our Latest Stock Report on QMLS

QumulusAI Stock Performance

Shares of NASDAQ:QMLS opened at $6.53 on Wednesday. QumulusAI has a twelve month low of $5.06 and a twelve month high of $38.00. The stock has a 50 day simple moving average of $6.52.

QumulusAI (NASDAQ:QMLS – Get Free Report) last issued its quarterly earnings results on Tuesday, August 25th. The company reported ($0.72) earnings per share for the quarter. The company had revenue of $6.71 million for the quarter. On average, analysts forecast that QumulusAI will post -2.71 earnings per share for the current year.

QumulusAI Company Profile

(Get Free Report)

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.

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