QumulusAI Q3 2026 Earnings: Revenue Doubles, Positive EBITDA

Artificial Intelligence
Artificial Intelligence
Earnings
Earnings
IPO:
Jul 16, 2026
Industry:
COMPUTER PROCESSING & DATA PREPARATION

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QumulusAI Reports Doubled Revenue and First Positive Adjusted EBITDA in Q3 2026

ATLANTA, USA - QumulusAI (Nasdaq: QMLS), a neocloud infrastructure provider purpose-built for the AI computing era, today reported strong financial results for the third quarter ended September 30, 2026. The company announced that its quarterly revenue reached $14.8 million, marking the second consecutive quarter of sequential revenue doubling, and reflecting a substantial 376% increase compared to the same period last year. Notably, QumulusAI achieved its first positive Adjusted EBITDA of $3.7 million.

Michael Maniscalco, CEO of QumulusAI, highlighted the effectiveness of their business model, stating, "Revenue doubled again this quarter, and remaining performance obligations grew to $515.0 million from $173.1 million at the end of the second quarter. That's the model doing its job: we sell the capacity before we energize it, we deploy into pockets of existing power, and we bring it online in months, not years." He also noted that the third quarter was still early in the ramp-up, with compute capacity coming online throughout and into the fourth quarter.

Financial Highlights for Q3 2026

    Key Financial Performance:

  • Revenue: $14.8 million, a 376% increase from $3.1 million in Q3 2025. Compute power revenue surged to $14.1 million, representing approximately 96% of total revenue, up from $1.4 million (47%) in the prior year.
  • Operating Loss: Increased to $41.6 million from $2.8 million in Q3 2025, primarily due to a $13.4 million rise in depreciation and amortization for expanded HPC infrastructure, and higher general and administrative costs, including $26.4 million for common stock issued for direct listing professional services.
  • Net Loss: Stood at $14.4 million, compared to a net loss of $9.5 million in Q3 2025. This period included $32.3 million in non-cash gains from fair value changes of the convertible note, related option, and warrant liability.
  • Adjusted EBITDA: Reached a positive $3.7 million, marking the company's first positive quarter, a significant improvement from a loss of $0.6 million in Q3 2025, driven by compute revenue growth.

Recent Corporate Developments

    Operational Milestones:

  • Active Capacity: Reached 8 MW.
  • Cumulative Signed Customer Contract Value: Surpassed $500 million, reaching $528.7 million.
  • New Agreements: Signed $240.9 million in new three-year take-or-pay agreements, covering over 2,000 NVIDIA Blackwell B300 GPUs across two dedicated clusters.
  • Blackwell Agreement in Production: A $71.9 million, three-year Blackwell agreement was fully put into production, with all GPUs billing within three months of signing.
  • New Contracted Capacity: 1.2 MW contracted in Denver, ready for service in Q1, and 0.5 MW in Columbia, South Carolina, ready for service in December 2026.

QumulusAI positions itself as a distributed AI cloud platform, providing accelerated access to high-performance GPU compute. Its inference-first, demand-led deployment model allows the company to bring compute resources closer to customer demand, enabling AI teams and enterprises to scale production AI workloads efficiently with speed, flexibility, and control. This approach aims to overcome the capacity limitations often found in traditional centralized and hyperscale cloud models.

About QumulusAI, Inc.

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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