Introduction

Qubrid provides a unified environment where developers, researchers, and enterprises can build, deploy, manage, and scale AI/ML workloads with ease.

Unlike traditional cloud platforms, which are often generic and complex, Qubrid is purpose-built for AI - offering GPU-powered compute, AI-specific templates, serverless models, tool integrations, and enterprise-ready features like usage tracking, team collaboration, and on-premise deployment options.

This documentation will help you understand the platform, its core features, and how to effectively use it to accelerate your AI journey.

Start here

Go to Qubrid Platform and create an account.

Whether you are a solo developer testing models, a researcher experimenting with new architectures, or an enterprise team scaling production AI workloads, Qubrid provides the tools to make it simple, scalable, and cost-efficient.

AI Model Studio

Accelerate development, comparison, and deployment of generative AI and LLM workloads.

AI Compute Platform

Flexible Infrastructure Options - Workload Orchestration Across GPU, CPU, NeoCloud

Why Qubrid?

AI workloads are resource-intensive, fragmented, and expensive to manage on generic cloud platforms. Qubrid solves these challenges by offering:

1. AI-First Design
   Everything in Qubrid is built for AI/ML - from GPU scheduling to optimized inferencing pipelines.

2. Speed to Market
   With pre-configured templates, serverless APIs, and tool integrations, you can go from idea to deployment in hours, not weeks.

3. Scalability
   Run workloads of any size - from small experiments to large distributed GPU clusters - without manual configuration.

4. Cost Efficiency
   Pay only for what you use, with transparent credits and usage dashboards.

5. Collaboration
   Manage organizations, teams, roles, and shared credits, so multiple users can work seamlessly.

6. Flexibility
   Deploy in the Qubrid NeoCloud or bring it on-prem for enterprises needing private/hybrid setups.

Who is Qubrid For?

• Developers
  Quickly prototype, test, and deploy AI/ML models without worrying about GPU setup.

• Researchers
  Run experiments at scale on GPU clusters and validate results with optimized inference pipelines.

• Startups
  Bring AI-powered products to market faster with prebuilt workflows and cost-efficient compute.

• Enterprises
  Enable teams to collaborate, manage resources, and deploy production AI securely at scale.

Key Features at a Glance