EAGLE-Pangu: Accelerator-Safe Tree Speculative Decoding on Ascend NPUs
Autoregressive decoding remains a primary bottleneck in large language model (LLM) serving, motivating speculative decoding methods that reduce expensive teacher-model invocations by verifying multiple candidate tokens per step. Tree-structured speculation further increases parallelism, but is often brittle when ported across heterogeneous backends and accelerator stacks, where attention masking, KV-cache layouts, and indexing semantics are not interchangeable. We present EAGLE-Pangu, a reproducible system that ports EAGLE-3-style tree speculative decoding to a Pangu teacher backend on Ascend NPUs. EAGLE-Pangu contributes (i) an explicit branch/commit cache manager built on the Cache API,...
| Authors | Chang Han, Yijie Hu, Jingling Liu |
|---|---|
| Primary category | cs.LG |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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