Bole: Efficient Tree Speculation for Hybrid-Attention Language Models
Hybrid-attention large language models combine full attention with recurrent linear attention to reduce long-context inference costs, yet their autoregressive decoding remains memory-bound. Tree speculative decoding offers an attractive acceleration path, but existing tree-speculation systems are designed around the key--value caches of full-attention models. On hybrid models, they traverse recurrent layers branch by branch and materialize a full state for every proposal node, causing verification latency and transient memory to scale poorly with tree and batch sizes. We present Bole, a kernel--runtime co-design that enables efficient tree speculation for hybrid-attention LLMs. Bole trans...
| Authors | Li Wang, Yi Su, Xiabao Wu et al. |
|---|---|
| Primary category | cs.DC |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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