Splitting a GPU Between Two Computers: Exploring Feasible Solutions

Many users face the challenge of wanting to use a single, expensive GPU with two separate computers, often positioned closely to each other. The idea seems appealing, especially for those focused on high-performance computing, gaming, or professional tasks requiring significant graphics processing power. Unfortunately, the current hardware and architecture do not natively support directly sharing a single GPU between two physical systems without doing a physical switch, a cumbersome workaround that is frequently undesirable.

Potential Solutions for GPU Sharing

While direct sharing is not straightforward, there are alternative methods that can achieve the goal of using a single GPU on two separate computers. Here are some potential approaches:

1. GPU Virtualization

NVIDIA vGPU or AMD MxGPU:

If you have access to a data center-grade GPU, utilizing virtualization technologies such as NVIDIA vGPU or AMD MxGPU can be a powerful solution. These technologies allow multiple virtual machines to share a single GPU, effectively splitting the GPU's resources between multiple applications running in different VMs. While this is commonly used in server environments, it does require specific hardware and software setups. Considering the cost and complexity, this may be more suitable for corporate or professional environments with the necessary infrastructure.

2. Remote Desktop Solutions

Remote desktop solutions offer a simpler, albeit less direct, way to leverage the GPU's power from another machine. Tools like Parsec allow you to access a machine with a dedicated GPU from another machine, provided you have a stable network connection. This setup can be handy for general use and less intensive tasks, but it may not be ideal for highly interactive or performance-sensitive applications.

3. External GPU Enclosures and KVM Switches

External GPU Enclosures:

Some external GPU enclosures, designed for mobility, support multiple devices but often do not facilitate simultaneous use of the GPU across both systems. They typically require manual switching of the connection between the two computers. This setup can be a good solution temporarily, but it does not provide seamless GPU sharing as desired.

KVM Switches:

A KVM (Keyboard, Video, Mouse) switch can help share peripherals and monitors between two computers, but it does not solve the GPU sharing issue directly. This technology is primarily for swapping between multiple computers, not for sharing GPU resources.

4. Custom Solutions and PCIe Switches

Custom Solutions:

Some enthusiasts have created custom setups using PCIe switches, which can theoretically enable the sharing of a GPU between two systems. However, such setups require significant technical expertise and may not work with all GPUs or setups. In addition, the reliability and performance of custom solutions are not always guaranteed.

The Better Solution: Utilizing Two Powerful Machines

Creating a powerful, dual-computer system with a single powerful machine running two virtual machines, each allocated to a different use case, is often a more practical approach. This setup allows you to bifurcate resources according to your specific needs, making efficient use of the GPU and other system resources.

By setting up a powerful computer with capable virtualization software, you can dedicate one VM to one use case and the other VM to the second use case. This way, you can dynamically allocate and manage resources, providing a flexible and efficient solution without the need for manual GPU switching.

Conclusion

Currently, there is no simple, seamless plug-and-play solution to share a single GPU between two computers without physically moving it. The best approach depends on your specific use case and whether you need high performance or just occasional access to the GPU from another machine. For frequent switching, investing in a second GPU for the other system or using remote access solutions might be more practical. Optimized setups with multi-computer configurations and efficient resource allocation can offer a more flexible and resource-wise solution to this challenge.