Shares of QumulusAI Inc. (NASDAQ:QMLS – Get Free Report) have been assigned a consensus rating of “Hold” from the five ratings firms that are covering the firm, Marketbeat reports. One equities research analyst has rated the stock with a sell recommendation, one has given a hold recommendation and three have issued a buy recommendation on the company. The average 12 month price objective among brokerages that have covered the stock in the last year is $14.33.
A number of brokerages have weighed in on QMLS. Weiss Ratings assumed coverage on QumulusAI in a report on Monday, October 12th. They issued a “sell (e-)” rating on the stock. Compass Point began coverage on QumulusAI in a report on Friday, October 16th. They set a “buy” rating and a $9.00 price target for the company. B. Riley Financial started coverage on QumulusAI in a research report on Wednesday. They set a “buy” rating and a $11.00 price objective for the company. Chardan Capital restated a “buy” rating and set a $23.00 price objective on shares of QumulusAI in a research report on Wednesday, September 30th. Finally, Wall Street Zen lowered QumulusAI from a “hold” rating to a “sell” rating in a research note on Saturday.
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QumulusAI Stock Up 7.6%
QumulusAI (NASDAQ:QMLS – Get Free Report) last posted its quarterly earnings results on Tuesday, August 25th. The company reported ($0.72) earnings per share (EPS) for the quarter. The company had revenue of $6.71 million for the quarter. As a group, equities research analysts expect that QumulusAI will post -2.77 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.
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