> For the complete documentation index, see [llms.txt](https://docs.synerisai.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.synerisai.org/network-operation-of-syneris/building-a-custom-gpu-+-cpu-network.md).
# Building a Custom GPU + CPU Network
While leveraging external GPU networks like io.net offers immediate access to resources and accelerates AI model deployment, establishing an internal network provides the control, cost efficiency, and scalability necessary for sustained innovation. By leveraging the computational strength of an established network, Syneris can quickly develop and deploy AI models while focusing on network growth and product improvements without being limited by initial hardware constraints.
### Immediate Access to Resources
By tapping into the external GPU power, Syneris gains instant access to a scalable infrastructure capable of handling heavy AI workloads. This enables builders to create freely without being constrained by Syneris's processing capabilities.
Leveraging an external GPU network allows Syneris to easily scale resources as needed, ensuring that developers always have sufficient computational power to tackle complex tasks. Immediate access to robust infrastructure minimizes preparation time, enabling developers to quickly implement their projects.
### Quick AI Model Deployment
The combination of Syneris’s CPU resources with other GPU pools accelerates the development of AI models, allowing the project to expedite products like AI assistants and deep learning models.
By integrating CPU power with the parallel processing capabilities of GPUs, Syneris can streamline the construction and training of AI models, reducing the time from concept to finished product. With significant computational power from the GPU network, developers can experiment with multiple models and algorithms simultaneously, enabling them to explore more innovative and optimized solutions.
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