AI RESEARCH
SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding
arXiv CS.AI
•
ArXi:2604.09557v1 Announce Type: cross Speculative Decoding (SD) has emerged as a critical technique for accelerating Large Language Model (LLM) inference. Unlike deterministic system optimizations, SD performance is inherently data-dependent, meaning that diverse and representative workloads are essential for accurately measuring its effectiveness. Existing benchmarks suffer from limited task diversity, inadequate for throughput-oriented evaluation, and a reliance on high-level implementations that fail to reflect production environments. To address this, we.