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

By Qumulusai · Published 2026-02-18 · Updated 2026-06-22 · Software & Technology on MarketScale
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Creator hub: QumulusAI

> 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, encountered these challenges while scaling his company’s AI platform. Because Amberd operates its own…

## Key points

- Developing private LLMs on AWS can lead to GPU infrastructure constraints.
- Smaller companies struggle to access consistent high-performance computing resources.
- Amberd partnered with QumulusAI for priority GPU infrastructure.

Block Field

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.

Tags: Amberd AI, AWS, Large Language Models, Mazda Marvasti, QumulusAI

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