![[2504.20101] PlanetServe: A Decentralized, Scalable, and Privacy-Preserving Overlay for Democratizing Large Language Model Serving](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2504.20101] PlanetServe: A Decentralized, Scalable, and Privacy-Preserving Overlay for Democratizing Large Language Model Serving
Summary
The paper presents PlanetServe, a decentralized overlay for scalable and privacy-preserving serving of large language models (LLMs), addressing key challenges in accessibility and efficiency.
Why It Matters
As LLMs become more integral to various applications, the need for scalable and efficient serving solutions is critical, especially for smaller organizations. PlanetServe proposes a novel approach that democratizes access to LLMs while ensuring privacy and resource efficiency, potentially transforming how AI services are deployed.
Key Takeaways
- PlanetServe utilizes a decentralized architecture to enhance LLM serving scalability.
- The proposed GenTorrent system addresses key challenges like network organization and privacy.
- Evaluation shows a latency reduction of over 50% compared to traditional methods.
- The security features of the system add minimal overhead, maintaining efficiency.
- This work sets a precedent for future developments in democratizing AI technologies.
Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2504.20101 (cs) [Submitted on 27 Apr 2025 (v1), last revised 13 Feb 2026 (this version, v5)] Title:PlanetServe: A Decentralized, Scalable, and Privacy-Preserving Overlay for Democratizing Large Language Model Serving Authors:Fei Fang, Yifan Hua, Shengze Wang, Ruilin Zhou, Yi Liu, Chen Qian, Xiaoxue Zhang View a PDF of the paper titled PlanetServe: A Decentralized, Scalable, and Privacy-Preserving Overlay for Democratizing Large Language Model Serving, by Fei Fang and 6 other authors View PDF HTML (experimental) Abstract: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 resul...
