  Modern deep learning models, growing larger and more complex, have
demonstrated exceptional generalization and accuracy due to training on huge
datasets. This trend is expected to continue. However, the increasing size of
these models poses challenges in training, as traditional centralized methods
are limited by memory constraints at such scales. This paper proposes an
asynchronous decentralized training paradigm for large modern deep learning
models that harnesses the compute power of regular heterogeneous PCs with
limited resources connected across the internet to achieve favourable
performance metrics. Ravnest facilitates decentralized training by efficiently
organizing compute nodes into clusters with similar data transfer rates and
compute capabilities, without necessitating that each node hosts the entire
model. These clusters engage in $\textit{Zero-Bubble Asynchronous Model
Parallel}$ training, and a $\textit{Parallel Multi-Ring All-Reduce}$ method is
employed to effectively execute global parameter averaging across all clusters.
We have framed our asynchronous SGD loss function as a block structured
optimization problem with delayed updates and derived an optimal convergence
rate of $O\left(\frac{1}{\sqrt{K}}\right)$. We further discuss linear speedup
with respect to the number of participating clusters and the bound on the
staleness parameter.
