AIoAI, Your First AI Appliance

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No cluster, cut down 90% cost

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Affordable

  • 90% reduction in deployment cost

    Training a 110B large-scale model traditionally requires a 2,200GB GPU memory pool—equivalent to around 30 high-end GPUs (e.g., 24 H100 cards as used in NVIDIA DGX systems)—deployed across 2–3 units of 4U servers, with a total cost of ¥8–10 million.

    In contrast, Ridger’s proprietary AI-HCM architecture enables training the same model with a single blade server equipped with four RTX 5880 GPUs (192GB total memory), reducing hardware cost to just ¥500,000–800,000.
    This means over 90% reduction in GPU cost, significantly lowering the entry barrier to LLM training.

  • 90% lower TCO compared to traditional solutions

    Traditional training clusters consume tens of kilowatts of power—driving up electricity costs, requiring electrical and cooling system upgrades, taking up significant space, and increasing maintenance complexity.
    Ridger’s proprietary AI-HCM architecture is engineered for energy efficiency from the ground up—significantly reducing TCO (Total Cost of Ownership):

    • Only 1kW power consumption — saves electricity and avoids costly power upgrades
    • No complex cooling or dedicated server room needed — quick to deploy in any environment
    • Integrated hardware–software design — fewer components, fewer failures, easier maintenance

    Lower energy use, faster deployment, smaller footprint, simpler operations — Ridger compresses long-term operational costs at the source, making large model deployment truly practical and sustainable.

Efficient

Advanced Automation Toolkit

  • Automatically detects system configurations and intelligently selects optimal training parameters based on user choices and training data
  • Supports one-click reset to revert to recommended default settings instantly
  • Offers advanced parameter customization for expert users who need fine-grained control
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Model-as-a-Service (MaaS) Engine

  • Fully compatible with mainstream large models from 6B to 110B parameters, supporting one-click deployment for instant use
  • Seamless integration with models of various sizes and architectures, including LLaMA, DeepSeek, Yi, and more
  • Unified scheduling across model architectures, enabling flexible switching and concurrent execution of models at different scales
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Ridger’s Custom-Built UI

Ridger’s custom-built UI provides a fully visual workflow—from model import and parameter setup to training and inference validation.
No command-line or coding required—deploy and train multiple models with zero technical barriers.

  • Model Repository Management: Easily import, organize, browse, and delete models
  • Training Monitoring: Live status updates on training progress, parameters, GPU, and memory usage
  • Multi-language Support: Seamless switching between Chinese and English interfaces for global accessibility
  • Lightweight Deployment: Web-based interface that runs directly in the browser and is easy to integrate with private compute environments
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