ASPIRE: Asynchronous Batched Self-Speculative Decoding for Long-Context LLM Inference

2026-09-15

AI Summary

为解决长上下文LLM推理中的注意力瓶颈问题，提出ASPIRE框架，通过异步批处理自推测解码方法提高解码吞吐量。

Abstract

Long-context LLM inference is bottlenecked by attention, whose repeated KV-cache reads make decoding memory-bound. Self-speculative decoding alleviates this by drafting tokens with sparse attention and verifying them with full attention, but existing batched methods remain synchronized: all requests in a batch share a single draft-verify schedule, even though the optimal draft length varies widely across requests and changes dynamically within each request. We propose ASPIRE, a non-synchronized batched self-speculative decoding framework built on three components. First, a unified mixed forward allows drafting and verifying requests to coexist in the same batched forward pass, removing the need for global draft-verify phases. Second, a lightweight online speculation scheduler uses per-request acceptance-rate estimates and a batch-aware cost model to let each request independently choose when to verify. Third, an intra-draft refresh layer performs full attention at a single designated layer during drafting, updating the sparse context at every draft step to reduce staleness during drafting. Across three models and five reasoning and long-context benchmarks, ASPIRE achieves $1.70$-$4.58\times$ speedup in decoding throughput over autoregressive baselines and improves average speedup by approximately $27\%$ over the strongest prior self-speculative baselines.

Problem

• long-context LLM inference
• attention mechanism
• KV-cache reads
• self-speculative decoding
• synchronized batched methods

Innovation

• asynchronous batched self-speculative decoding
• unified mixed forward
• lightweight online speculation scheduler
• intra-draft refresh layer