# QumulusAI Secures Priority GPU Infrastructure Amid AWS Capacity Constraints on Private LLM Development

Published 2026-02-18 · Updated 2026-06-22 · Engineering & Construction on MarketScale
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Creator hub: QumulusAI

> Smaller firms building private AI models can now bypass the GPU bottleneck that slows larger cloud competitors

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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**](https://www.linkedin.com/in/mazda-marvasti-ph-d-308161/), CEO of [**Amberd**](https://amberd.ai/), encountered these challenges while scaling his company's AI platform. Because Amberd operates its own private LLM, the team required dependable, dedicated GPU capacity rather than shared cloud resources. Marvasti says limited GPU access created delays and operational uncertainty. He ultimately turned to [**QumulusAI**](https://www.qumulusai.com/) for a more predictable alternative. The move provided priority, fixed-cost GPU infrastructure, enabling Amberd to deliver dedicated environments where customers retain ownership of both the machines and their data.

> Limited GPU access created delays and operational uncertainty.

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