Moreh provides a full-stack AI infrastructure platform that integrates PyTorch with GPU virtualization to facilitate the scaling of large language models and AI applications. The platform addresses the challenge of accessibility and resource allocation in hyperscale AI environments, enabling efficient fine-tuning and deployment across multiple GPUs.
Funding
$26.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Training and deploying large language models (LLMs) requires significant computational resources, making it challenging for many organizations to efficiently scale their AI initiatives. Existing AI infrastructure often lacks the flexibility to handle diverse hardware configurations and fine-grained resource allocation, leading to underutilization and increased costs.
Solution
Moreh provides a full-stack AI infrastructure platform, called MoAI, designed to streamline the development and deployment of LLMs. The platform integrates PyTorch with GPU virtualization, enabling efficient scaling and resource management across heterogeneous GPU environments. MoAI facilitates fine-tuning and deployment of AI models, addressing the accessibility and resource allocation challenges in hyperscale AI environments. By offering comprehensive GPU virtualization, including fine-grained resource allocation and multi-GPU scaling, Moreh empowers organizations to optimize their AI infrastructure and reduce operational costs.
Target Audience
The primary target audience includes organizations and developers working with large language models and AI applications who require scalable and efficient AI infrastructure solutions.
Features
- Full-stack infrastructure software from PyTorch to GPUs
- Comprehensive GPU virtualization with fine-grained resource allocation
- Multi-GPU scaling for large language model training
- Support for heterogeneous GPU environments
- Tools for efficient fine-tuning and deployment of AI models