This paper focuses on developing the peer-to-peer rendering and computation (P2PRC) framework, which is a distributed framework for executing computationally demanding tasks that a personal machine with limited processing power will struggle to run such as graphically demanding video games, rendering 3D animations, and protein folding simulations. A custom peer-to-peer network was implemented to decentralize the execution of tasks either on central processing unit (CPU) or graphical processing unit (GPU), in order to increase the bandwidth for running tasks. To prevent the tasks in the peer-to-peer network from corrupting the operating system (OS) of the server, they will be executed in a virtual environment in the server. The user acting as the client is provided full flexibility on how to batch the tasks, and the user acting as the server has complete flexibility on tracking the container’s usage and killing the containers at any time. The effectiveness of the network and the performance of the distributed task execution of the distributed framework were evaluated using Horovod and TensorFlow benchmarks. Preliminary results are very promising with 86 and 97% improvements for CPU and GPU distribution, respectively.