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

QumulusAI (NASDAQ:QMLS – Get Free Report)‘s stock had its “buy” rating reissued by equities researchers at Chardan Capital in a research report issued on Wednesday, Benzinga reports. They currently have a $23.00 price target on the stock. Chardan Capital’s price objective would indicate a potential upside of 245.03% from the stock’s current price.

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

Get Our Latest Stock Analysis on QMLS

QumulusAI Stock Up 2.1%

NASDAQ:QMLS traded up $0.14 on Wednesday, reaching $6.67. The stock had a trading volume of 96,668 shares, compared to its average volume of 501,533. The stock’s fifty day simple moving average is $6.52. QumulusAI has a 1-year low of $5.06 and a 1-year high of $38.00.

QumulusAI (NASDAQ:QMLS – Get Free Report) last announced 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. On average, research analysts expect that QumulusAI will post -2.71 EPS for the current fiscal year.

About QumulusAI

(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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