gradient.networkTurning Latency into Throughput: Speculative Decoding for the Decentralized Inferencehttps://arxiv.org/abs/2511.11733 The Latency Wall In centralized inference, speed is mostly a function of compute. You optimize by saturating HBM bandwidth, fusing kernels, and keeping GPUs close to their roofline. In decentralized inference, where...Nov 24, 2025·6 min read
gradient.networkLattica: A Universal Communication Substrate for Open Intelligencehttps://arxiv.org/abs/2510.00183 Lattica is a communication tool that solves internet barriers like firewalls. It lets scattered computers securely connect and work together to run powerful AI, creating a global, open network. Introduction: The Mi...Oct 1, 2025·4 min read
gradient.networkMassgen: When Multiple LLMs Think Togetherhttps://arxiv.org/abs/2509.23537 Massgen makes different AIs work together as a team, debating and voting on answers. This collaborative approach overcomes individual weaknesses, achieving smarter results than any single AI could alone Introductio...Sep 30, 2025·4 min read
gradient.networkParallax: Your Sovereign AI OShttps://arxiv.org/abs/2509.26182 Why this matters First things first: we want your personal AI agents to be sovereign. It should not upload everything it sees to a giant centralized cloud. What it learns about you should live as a portable local me...Sep 30, 2025·9 min read
gradient.networkVeri: The Trust Layer for Distributed Inferencehttps://arxiv.org/abs/2509.24257 VeriLLM is a verification protocol for large language models that run across a decentralized network of volunteer GPUs. Its goal is simple and strict: when you ask a model to run on untrusted hardware, the answer yo...Sep 28, 2025·7 min read
