How JPStream Resolved

Machine Learning and Artificial Intelligence

Challenge

Our client, heavily reliant on GPU servers for running resource-intensive AI/ML algorithms, faced significant performance issues when migrating to the cloud. Despite utilizing cloud GPU servers, they experienced slow processing times, unreliable resource scaling, and high operational costs. The performance bottleneck threatened their project timelines and customer satisfaction.

Challenge, Solution and Result

Project Information

Client:

AI and ML solutions company

Location:

Japan

Industry

Artificial Intelligence & Machine Learning (AI/ML)

Date

November 2024

Interested
  • Challenges
  • Requirements
  • Results
  • Conclusion
  • Key Takeaway

Key Problems Identified

  • Inadequate GPU Optimization: The cloud GPU servers were not optimized for handling parallel processing of AI workloads.
  • Cost Escalation: Despite high usage, the client was facing unpredictable cost spikes due to inefficient GPU allocation and usage.
  • Limited Scalability: As project requirements grew, the existing cloud setup failed to scale GPU resources smoothly.
  • Latency Issues: Delays in data processing caused disruptions in delivering AI insights in real-time.

    • Technical situation and environment analysis

    JPStream stepped in to analyze the existing cloud infrastructure and GPU server setup. By leveraging our expertise in dedicated GPU cloud servers, we deployed a customized solution for the client.

  • Optimized GPU Configuration: JPStream restructured the GPU allocation to ensure it was optimized for their specific AI/ML workloads. We configured dedicated servers with GPU resources that precisely matched their parallel computing needs.
  • Scalable Cloud Infrastructure: We implemented an auto-scaling system, allowing the client to scale up or down GPU resources based on real-time demand, avoiding bottlenecks while maintaining cost efficiency.
  • Enhanced Performance Monitoring: JPStream integrated real-time performance monitoring tools, allowing the client to track GPU usage and performance, which improved overall processing speeds and reduced downtime.
  • Cost Efficiency: Through strategic resource allocation and monitoring, JPStream reduced unnecessary GPU usage and eliminated cost overruns, saving the client approximately 25% in operational expenses

  • Improved Performance: Processing times were reduced by 35%, enabling faster AI/ML model training and data analysis.
  • Cost Reduction: The client experienced a 25% reduction in overall operational costs through optimized GPU usage and scalable solutions.
  • Seamless Scalability: The client could now easily scale up their GPU resources during peak workloads and scale down when not in use, without experiencing performance issues.
  • Increased Customer Satisfaction: The timely resolution of performance bottlenecks improved the client’s ability to deliver projects on schedule, enhancing customer satisfaction.

    • By implementing JPStream’s powerful GPU cloud servers, the client overcame their performance issues, reduced operational costs, and successfully scaled their AI/ML workloads. Our dedicated support and expertise ensured that the client could focus on innovation rather than infrastructure challenges.

    JPStream offers tailored cloud GPU server solutions that provide optimized performance, scalability, and cost efficiency for businesses running resource-heavy workloads like AI and ML. Whether you’re migrating to the cloud or improving your existing setup, we have the expertise to elevate your infrastructure.

    JPStream Cloud GPU
    For AI and ML Industry

    Virtualized GPU technology for sharing graphics processing & computing power.

    Most Recommended

    vGPU 16Q Plan

    ¥ 128728 / Monthly
    • GPU Memory: 16GiB
    • Memory RAM: 48GiB
    • CPU: 8 vCPU
    • Storage: 800GiB NVMe SSD
    • Transfer: 10TB
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    High Performance

    vGPU 48Q Plan

    ¥ 278988 / Monthly
    • GPU Memory: 48GiB
    • Memory RAM: 128GiB
    • CPU: 24 vCPU
    • Storage: 1500GiB NVMe SSD
    • Transfer: 16TB
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    Dedicated GPU

    Dedicated GPU Entry Level

    ¥ 78228 / Monthly

    Achieve Ultra-High Performance

    • Let your cloud server harness the full power of dedicated GPU to achieve ultra-high-performing parallel data computation, machine learning and graphics-intensive projects.
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