ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios
Speculative Decoding promises to accelerate the inference of Large Language Models, yet its efficacy often degrades in production-grade serving. Existing evaluations typically overlook the compute-bound nature of high-concurrency regimes, where verification compute becomes the dominant bottleneck. Consequently, prior methods face a dilemma: static trees incur massive verification waste, while dynamic trees suffer from cumulative misjudgments and kernel incompatibility. To bridge this gap, we introduce ECHO, a high concurrency-oriented framework integrated into SGLang that reformulates speculative execution as a budgeted scheduling problem. Crucially, ECHO employs sparse confidence gating ...
| Authors | Xinyi Hu, Yuhao Shen, Baolin Zhang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 10 March 2026 |
| Last updated at source | 14 May 2026 |
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