Introducing SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding
Why It Matters
SPEED-Bench provides a comprehensive and realistic evaluation framework for speculative decoding, enabling better analysis and comparison of SD algorithms and models.
Release Summary
SPEED-Bench is a new benchmark for evaluating speculative decoding (SD) in AI models.
It addresses gaps in existing benchmarks by focusing on semantic diversity and realistic serving conditions.
The benchmark includes a 'Qualitative' data split for measuring speculation quality and a 'Throughput' data split for evaluating system-level speedups.
SPEED-Bench uses production-grade inference engines to standardize evaluation across systems.
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