The rise of AI and large language models (LLMs) has driven the rapid adoption of AI applications, which rely heavily on GPUs for their operation. As a result, GPU scheduling, management, monitoring, fault alerts, and performance testing have become essential. While many companies have developed tools to address these needs, such solutions typically depend on real GPUs.

However, GPUs are far scarcer and more expensive than CPUs. Testing these functionalities, while crucial, often results in resource wastage. Moreover, limited access to diverse GPU types and configurations makes it challenging to perform large-scale validation across multi-card, multi-node, and heterogeneous GPU environments.

The ecosystem currently lacks an open-source project for GPU information simulation.
To address this, we introduce Fake-GPU, an open-source project that simulates GPU runtime environments non-intrusively.