The startup provides a hybrid cloud platform that combines GPU cloud services with AI controller software for on-premises deployments, enabling enterprises to efficiently manage AI workloads. This solution allows businesses to optimize their data center resources while seamlessly scaling to the public cloud as needed.
Funding
$25.5M 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
Enterprises face challenges in efficiently managing and deploying AI workloads across diverse environments, including on-premises data centers and public clouds. Optimizing the utilization of expensive GPU resources and ensuring seamless scalability between local and cloud infrastructure adds further complexity. Managing AI packages and keeping them up to date also creates headaches for IT.
Solution
Qubrid AI provides a hybrid AI platform that combines on-premises AI appliances with a cloud-based GPU platform, enabling organizations to manage AI workloads across edge-to-cloud environments. The platform features an AI Controller software for on-premises GPU management, allowing administrators to monitor and control GPU resources from a centralized console. Users can deploy open-source AI models, fine-tune models with no-code tools, and leverage NVIDIA NIM microservices. The cloud component offers on-demand access to a range of NVIDIA GPUs, including H100, A100, and L40S, facilitating scalable AI development and deployment. Qubrid AI simplifies AI infrastructure management, enabling users to focus on innovation rather than the complexities of resource allocation and package management.
Target Audience
The primary target audience includes AI developers, IT administrators, and data scientists in enterprises who need a comprehensive platform for managing and deploying AI workloads across hybrid environments.
Features
- AI Controller software for on-premises GPU server management, featuring a centralized console for monitoring and control
- Automated deployment of GPU clusters and streamlined updates for operating systems, GPU drivers, and AI packages
- Flexible container provisioning for developers, with tailored compute containers and resource allocation control
- Support for NVIDIA NIM microservices, enabling accelerated deployment of foundation models
- No-code fine-tuning and RAG capabilities, allowing users to fine-tune models and implement retrieval-augmented generation without programming expertise
- Integration with Hugging Face, allowing users to deploy AI models directly from the Hugging Face repository
- One-touch deployment of complete AI/ML deep learning packages, including PyTorch, TensorFlow, and Keras
- On-demand GPU cloud platform with a range of NVIDIA GPUs, including H100, A100, L40S, and T4