> For the complete documentation index, see [llms.txt](https://dc-ai.gitbook.io/dcai-ecosystem/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dc-ai.gitbook.io/dcai-ecosystem/technical-infrastructure/ai-training-on-distributed-node.md).

# AI training on Distributed node

* Enables high-performance machine learning workloads by leveraging decentralized computing resources.
* Distributed compute layer that allows Al models to be trained on decentralized nodes. 
* Capable of handling large-scale machine learning workloads by leveraging the compute power of GPUs integrated with DCAl's enterprise mining devices. 
* The power distribution within the network is public and verifiable, allowing anyone to audit the storage assignments and ensure transparency. 
* DCAl adopts a system where the influence of each participant, or miner, is determined by the amount of storage they contribute to the network.

<figure><img src="/files/ir3qu0OQkgKDwiuoxxHd" alt=""><figcaption></figcaption></figure>

* A comprehensive platform for AI training and decentralized storage that shifts operational control to community members. The DCAI network offers a distributed system for scalable and secure data and compute services.

<figure><img src="/files/pKfjKc2TWoGPd0JzJEbw" alt=""><figcaption></figcaption></figure>