Accelerating Training and Inference of EAGLE-3 for Multi‑Round Agent Workflows
This article analyzes the latency bottlenecks of large language models in multi‑round AI Agent scenarios, introduces SpecForge‑based speculative decoding and Unified Sequence Parallelism (USP) techniques applied to the EAGLE-3 model, and presents benchmark results showing over two‑fold Accept‑Len gains and 35‑44% reductions in P95 token‑level latency while enabling 128K context training on an 8‑GPU node.
