Amberd Moves to the Front of the Line With QumulusAI’s GPU Infrastructure

 

Reliable GPU infrastructure determines how quickly AI companies can execute. Teams developing private LLM platforms depend on consistent high-performance compute. Shared cloud environments often create delays when demand exceeds available capacity

Mazda Marvasti, CEO of Amberd, says waiting for GPU capacity did not align with his company’s pace. Amberd required guaranteed availability to support its private LLM platform. Cost predictability was equally important. Marvasti turned to QumulusAI to secure priority, fixed-cost GPU infrastructure. He says this approach removed uncertainty around GPU availability and stabilized expenses. The model allows Amberd to move quickly while passing predictable infrastructure costs to customers.

 

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Unpredictable AI costs have become a growing concern for organizations running private LLM platforms. Usage-based pricing models can drive significant swings in monthly expenses as adoption increases. Budgeting becomes difficult when infrastructure spending rises with every new user interaction. Mazda Marvasti, CEO of Amberd, says pricing volatility created challenges as his team expanded its…

Developing a private large language model(LLM) on AWS can expose infrastructure constraints, particularly around GPU access. For smaller companies, securing consistent access to high-performance computing often proves difficult when competing with larger cloud customers. Mazda Marvasti, CEO of Amberd AI,  encountered these challenges while scaling his company’s AI platform. Because Amberd operates its own…

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