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Documentation Index

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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, managed agents, RAG workflows, 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.

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AI Model Studio

Accelerate Development, Fine-tuning, and Deployment of Generative AI & LLM Models.

Frictionless Model Building

Craft your own custom models with a user-friendly, code-optional interface.

Seamless Deployment

Integrate trained models into your applications with a few clicks, accelerating the path to market.

Pre-trained Models

Jumpstart your project with a library of industry-leading models for tasks like image recognition, natural language processing, and more.

Advanced Training & Tuning

Fine-tune pre-trained models or your own creations with robust optimization tools and access to high-performance compute resources.

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AI Compute Platform

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

Diverse Compute Instances

Choose from a range of on-demand compute instances for general development.

Optimized Inference GPUs

Deploy models with lightning-fast performance using optimized inference GPUs.

Specialized GPUs

Access cutting-edge NVIDIA, AMD & Intel GPUs designed for training & tuning LLMs.

Quantum Simulations

Conduct quantum simulations on GPUs & access real quantum computing resources (FCFS).

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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, RAG workflows, and one-click agents, 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.

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Who is Qubrid For?

  • Developers
    Quickly prototype, test, and deploy AI/ML models without worrying about GPU setup.
  • Researchers
    Run experiments at scale, fine-tune open-source models, and validate results with optimized 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.

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Key Features at a Glance

GPU Infrastructure

• Compute Scaling
• Instance Clusters
• Credit Billing

AI Templates

• Prebuilt Models
• Fast Tuning
• One-Click Deploy

RAG Workflows

• Smart Agents
• Knowledge Retrieval
• Context Apps

User Dashboard

• Resource Visibility
• Usage Tracking
• Team Access

App Integrations

• Hugging Face
• API SDKs
• Event Webhooks

Data Security

• Role Control
• Data Encryption
• Compliance Ready