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How TenClass saved 80% on GPU costs with TensorFusion?
2025/09/01

How TenClass saved 80% on GPU costs with TensorFusion?

TenClass using TensorFusion to save 80% on GPU costs

Background

TenClass is a vocational education company based in Shenzhen, China, providing digital literacy and professional skills courses to help users enhance their digital skills.

Problem

To provide a convenient and high-quality hands-on learning environment, TenClass developed a virtual machine manager mvisor, but in the construction of hands-on learning environments for AI drawing and AI video courses, due to the need for direct GPU access in online laboratories, due to the immaturity of GPU virtualization technology, providing a dedicated AI drawing environment for each learner is very difficult and expensive.

Solution

After learning that TensorFusion can achieve GPU virtualization and pooling, after a period of evaluation and implementation, TenClass became the first customer of TensorFusion.

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TensorFusion provided the following solution to TenClass, solving the cost problem and GPU availability of cloud hands-on learning environments:

  • Build a GPU pool of T4 GPUs in Linux environment
  • Each T4 is cut into 2 or 3 virtual GPUs, cooling the GPU memory to host memory when not active, suspending the virtual GPU power
  • Each user's exclusive Windows virtual machine hands-on learning environment, built-in TensorFusion Client, automatically connecting to virtual GPU when starting or executing drawing workflow, and getting AI computing power

Results

Before using TensorFusionAfter using TensorFusion
😕 Have to buy GPU from cloud vendor for every learner😊 Save >80% costs
😕 GPU not available sometimes, impacting user experience😊 With GPU pooling, AI computing availability increased to 99.9%
😕 Long set up time of new hands-on lab environment😊 Reduced 45% setup time for new hands-on lab
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Author

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Tensor Fusion

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