QumulusAI (NASDAQ:QMLS) Rating Lowered to Sell at Wall Street Zen

QumulusAI (NASDAQ:QMLS) was downgraded by research analysts at Wall Street Zen from a “hold” rating to a “sell” rating in a report issued on Saturday, Wall Street Zen reports.

Other equities research analysts also recently issued reports about the company. Compass Point initiated coverage on QumulusAI in a report on Friday. They set a “buy” rating and a $9.00 target price for the company. Chardan Capital reissued a “buy” rating and issued a $23.00 price objective on shares of QumulusAI in a research note on Wednesday, September 30th. Weiss Ratings started coverage on QumulusAI in a report on Monday. They issued a “sell (e-)” rating on the stock. Finally, Zacks Research upgraded shares of QumulusAI to a “hold” rating in a research report on Friday, August 28th. Two equities research analysts have rated the stock with a Buy rating, one has assigned a Hold rating and one has assigned a Sell rating to the stock. According to data from MarketBeat, the company presently has an average rating of “Hold” and an average price target of $16.00.

QumulusAI Price Performance

QMLS opened at $3.81 on Friday. The business has a 50 day moving average price of $6.09. QumulusAI has a 1-year low of $3.70 and a 1-year high of $38.00.

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 for the quarter. On average, analysts predict that QumulusAI will post -2.93 EPS for the current fiscal 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.