Exploring the Possibilities of Sharing GPU Resources among Virtual Machines

Honyee Chua

Hatched by Honyee Chua

May 02, 2024

3 min read


Exploring the Possibilities of Sharing GPU Resources among Virtual Machines


In recent years, there has been a growing demand for efficiently utilizing GPU resources among multiple virtual machines. This article aims to discuss the concept of sharing GPU resources and explore various methods to enable this functionality. We will delve into the topic by analyzing the "Supporting `*.safetensors` format" pull request by Narsil and the discussion on the Enshan Wireless Forum regarding GPU sharing using the GVT technology.

The Pull Request:

The "Supporting `*.safetensors` format" pull request by Narsil on the AUTOMATIC1111 repository highlights the importance of enabling support for the `*.safetensors` format. While the specific details of the pull request may not be directly related to GPU sharing, it is crucial to consider the broader context. By incorporating standardized formats such as `*.safetensors`, we can ensure compatibility and ease of use when implementing GPU sharing functionality.

The GVT Technology:

The Enshan Wireless Forum discussion focuses on the GVT technology, which allows multiple virtual machines to share a single GPU simultaneously. Similar to the concept of SR-IOV for network cards, GVT enables the direct pass-through of a GPU to multiple virtual machines. This technology leverages the capabilities supported by the system's BIOS, making it widely accessible for various x86 systems, including OpenWrt (x86) and Router OS.

Connecting the Dots:

Although the "Supporting `*.safetensors` format" pull request and the GVT discussion appear to be distinct, they share a common goal - enabling efficient GPU resource sharing among virtual machines. By incorporating standardized formats, such as `*.safetensors`, into the implementation of GVT technology, we can enhance compatibility and streamline the GPU sharing process. This convergence of ideas highlights the importance of collaboration and integration in advancing GPU resource utilization.

Unique Insights:

While the existing content provides valuable information, we can further enhance this article by incorporating unique insights into the topic. One interesting aspect to consider is the potential impact of GPU sharing on various industries. For instance, in the field of artificial intelligence and machine learning, GPU-intensive tasks can benefit significantly from the ability to share GPU resources among multiple virtual machines. This can lead to improved efficiency and reduced costs for organizations working with large-scale GPU-based workloads.

Actionable Advice:

  • 1. Ensure Compatibility: When implementing GPU resource sharing among virtual machines, it is essential to prioritize compatibility. By adopting standardized formats, such as `*.safetensors`, and leveraging technologies like GVT, organizations can enhance compatibility and ensure seamless integration between different virtual machine instances.
  • 2. Optimize Resource Allocation: To effectively share GPU resources, it is crucial to optimize resource allocation among virtual machines. Organizations should consider workload balancing techniques, such as dynamic resource allocation and workload migration, to ensure equitable access to GPU resources and maximize overall system performance.
  • 3. Regularly Update BIOS: As GVT relies on BIOS support for enabling GPU sharing, it is essential to keep the system's BIOS up to date. Regularly checking for BIOS updates and applying them as necessary will ensure compatibility with the latest GPU sharing technologies and improvements.


Efficiently sharing GPU resources among virtual machines is a compelling concept with significant potential benefits for various industries. By considering the insights provided by the "Supporting `*.safetensors` format" pull request and the GVT discussion, we can explore ways to enhance compatibility and streamline the GPU sharing process. By following actionable advice such as prioritizing compatibility, optimizing resource allocation, and keeping the BIOS up to date, organizations can unlock the full potential of GPU resource sharing and drive advancements in GPU-based workloads.

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