SPEED-Bench: Unified Benchmark for Speculative Decoding

SPEED-Bench is presented as a novel benchmark designed to address the limitations of existing evaluation methods for speculative decoding (SD). SD uses a draft model to predict future tokens, which are then verified by a target model, improving throughput while maintaining output quality. The benchmark consists of two data splits: a 'Qualitative' split focused on semantic diversity and speculation quality, and a 'Throughput' split designed to evaluate system-level speedups under various input sequence lengths and concurrency levels. SPEED-Bench also includes a unified measurement framework integrated with production inference engines, standardizing evaluation across different systems. This comprehensive approach aims to provide a more realistic and representative assessment of SD algorithms, enabling researchers and practitioners to better understand their behavior and optimize their performance in real-world scenarios. By addressing the fragmentation and limitations of existing benchmarks, SPEED-Bench has the potential to accelerate progress in SD research and development, leading to more efficient and performant large language models.

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