However,batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately,efficient model-parallel algorithms tend tobe complicated todiscover, describe, and to implement, particularly on large clusters.

While significant progress has been made in research and development on open-source and cost-efficient large-language models (LLMs), serving scalability remains a critical challenge, particularly for small organizations and individuals seeking to deploy and test their LLM innovations. Inspired by peer-to-peer networks that leverage decentralized overlay nodes to increase throughput and availability, we propose GenTorrent, an LLM serving overlay that harnesses computing resources from decentralized contributors. We identify four key research problems inherent to enabling such a decentralized infrastructure: 1) overlay network organization; 2) LLM communication privacy; 3) overlay forwarding for resource efficiency; and 4) verification of serving quality. This work presents the first systematic study of these fundamental problems in the context of decentralized LLM serving. Evaluation results from a prototype implemented on a set of decentralized nodes demonstrate that GenTorrent achieves a latency reduction of over 50% compared to the baseline design without overlay forwarding. Furthermore, the security features introduce minimal overhead to serving latency and throughput. We believe this work pioneers a new direction for democratizing and scaling future AI serving capabilities.

Zanskar uses AI to identify hidden geothermal systems--and claims it has found one that could fuel a power plant, the first such discovery by industry in decades. A geothermal startup said Thursday that it has hit gold in Nevada--metaphorically speaking. Zanskar, which uses AI to find hidden geothermal resources deep underground, says that it has identified a new commercially viable site for a potential power plant. The discovery, the company claims, is the first of its kind made by the industry in decades. The find is the culmination of years of research on how to find these resources--and points to the growing promise of geothermal energy .

High network communication cost for synchronizing gradients and parameters is the well-known bottleneck of distributed training.

All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to discover, describe, and to implement, particularly on large clusters.

Raul Rojas Department of Mathemanullcs and Stanullsnullcs University of Nevada Reno October 2025 Abstract This paper shows how a mulnulllayer perceptron with two hidden layers, which has been designed to classify two classes of data points, can easily be transformed into a deep neural network with one - gate layers and skip connecnullons. As shown in [1], deep one - gate per layer networks can perfectly separate points belonging to two classes in an n - dimensional space. Here, I present an alternanullve proof that may be easier to understand. This proof shows that classical neural networks that separate two classes can be transformed into deep one - gate - per - layer networks with skip connecnullons. A perceptron receives a vector input and divides input space into two subspaces: the posinullve and neganullve half - spaces (Figure 1a).