MotusAI

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What is MotusAI

What is MotusAI

MotusAI is an enterprise-grade AI platform that offers full control and transparency over your AI infrastructure, workloads, and users. Designed for industries like security, financial services, energy, automotive, and education, it delivers a high-performance, scalable, and secure solution.
What is MotusAI
01.
What is MotusAI?
01.
What is MotusAI?
02.
Improving Resource Utilization through GPU Sharing
02.
Improving Resource Utilization through GPU Sharing
03.
Quota Management for User/User Group
03.
Quota Management for User/User Group
04.
Cluster Monitoring and Reports
04.
Cluster Monitoring and Reports
05.
Workspace for Model Developing
05.
Workspace for Model Developing
06.
Model Training for Single-node and Distributed
06.
Model Training for Single-node and Distributed
07.
Model Deployment and Service Management
07.
Model Deployment and Service Management
08.
A/B Testing for Updating Model Service
08.
A/B Testing for Updating Model Service

Key Features

  • Visual Operation Interface

    MotusAI offers a user-friendly, low-code platform that enables one-click service operations and maintenance. It provides a simple, intuitive interface for users to comprehensively monitor AI cluster status and workloads, helping enterprises reduce operational management costs.

  • AI Infrastructure Resource Scheduling

    MotusAI integrates various task scheduling mechanisms, such as job queuing, GPU load balancing, dataset affinity scheduling, and so on. These features optimize AI cluster resource utilization for enterprises.

  • GPU Pooling and Fractioning

    MotusAI supports workloads of varying scales, from large-scale distributed training tasks to fine-grained GPU task partitioning, ensuring stable and efficient operation at all levels.The GPU segmentation strategy can increase the resource utilization rate by 400% in the scenarios of algorithm development and service deployment.

  • End-to-End AI Workflow

    MotusAI supports the full lifecycle of large language models, including pre-training, fine-tuning, testing, model export, deployment, and Chat/Prompt management. It offers code-driven and configurable processes, ensuring compatibility with widely used LLMs such as DeepSeek, LLaMA or ChatGPT.

Performance

  • Batch Scheduling Latency Reduced By 14×

    MotusAI Batch Scheduling Latency Reduced By 14×

    Batch Scheduling Latency Reduced By 14×

    Compared to the open-source scheduler Kube-batch, MotusAI was tested under high-concurrency scenarios with 1,000 and 5,000 pods. After optimization, MotusAI significantly reduced scheduling latency, with a maximum reduction of up to 14 times.
    14times

  • High-Concurrency Task Throughput By 13×

    MotusAI High-Concurrency Task Throughput By 13×

    High-Concurrency Task Throughput By 13×

    Compared to the open-source scheduler Kube-batch, MotusAI has optimized throughput performance for high-concurrency tasks. Under a workload of 5,000 concurrent pods, throughput has increased by more than 13 times.
    13times

  • Data Transfer Acceleration By 43%

    MotusAI Data Transfer Acceleration By 43%

    Data Transfer Acceleration By 43%

    Compared to direct data retrieval from shared storage, MotusAI optimizes the storage path by caching data on local nodes. For large-scale small file transfers, this improves performance by up to 43%.
    43%

  • P2P Download Efficiency

    MotusAI P2P Download Efficiency

    P2P Download Efficiency

    P2P may reduce efficiency in low concurrency, but greatly boosts performance under high concurrency. MotusAI P2P downloads outperform direct downloads, especially in high-load environments.
    P2Pdownloads

  • Distributed Training Scaling Reach 90%

    MotusAI Distributed Training Scaling Reach 90%

    Distributed Training Scaling Reach 90%

    For distributed training with ResNet50, data shows that under a scale of 800 GPU cards, MotusAI's linear acceleration ratio can still reach over 90%.
    90%

  • Training Efficiency Boost By 72%

    MotusAI Training Efficiency Boost By 72%

    Training Efficiency Boost By 72%

    For AI model training with ResNet50, Data shows that MotusAI's data caching strategy significantly boosts AI model training efficiency, especially in high-concurrency scenarios, with up to a 72% improvement.
    72%

Customer Stories

AI+Science Leap

By deploying the MotusAI platform, a pioneering research university achieved over 90% GPU utilization through intelligent scheduling and GPU sharing. The solution enabled parallel processing of 30+ high-priority tasks, slashed distributed training preparation from days to hours with adaptive parameter tuning, and ensured 90% valid training time via breakpoint continuation—accelerating critical research by 5x.
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Bank AI Transformation

With unified resource scheduling and automated fault recovery, MotusAI transformed the bank’s AI infrastructure, cutting model training cycles from one week to just one day. Accelerated image distribution and optimized networking amplified GPU efficiency by 7x, enabling seamless and rapid AI deployment across diverse business scenarios.
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AI Manufacturing Leap

The MotusAI platform revolutionized AI-driven manufacturing with intelligent resource scheduling, reducing data transfer latency by 30% while maximizing GPU utilization efficiency. Unified platform management accelerated distributed training setup time by 8x, enabling parallel development across 10+ scenarios and cutting model iteration cycles by 75%. This innovation-driven approach has accelerated agile 3D perception advancements, driving the next wave of intelligent manufacturing.
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Intelligent Computing for Autonomous Driving

With MotusAI, intelligent resource scheduling and GPU virtualization optimized computing efficiency, pushing GPU utilization to 90%. Training efficiency improved by 35%, while hyper-scale distributed task deployment enabled minute-level execution and supported 4-5 concurrent training tasks. By reducing development cycles by 67%, the solution significantly accelerated multi-vehicle ADAS deployment.
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Clinical AI Breakthrough

Powered by MotusAI, intelligent computing platforms optimized GPU elastic scheduling, increasing resource utilization to 80% and tripling training efficiency. The solution enabled concurrent multi-task development, breakpoint resumption, and secure data sharing, reducing project cycles by 67%. These advancements have accelerated the deployment of AI-driven applications, including surgical image recognition, enhancing precision and efficiency in clinical settings.
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  • Education
  • Finance
  • Manufacturing
  • Automotive
  • Healthcare

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