SlimSpec: faster speculative decoding without cutting the vocabulary

SlimSpec is a low-rank draft LM-head architecture designed to enhance speculative decoding by compressing the drafter's hidden representation without reducing its vocabulary, thereby maintaining full-vocabulary support while significantly reducing computational costs. In contrast to vocabulary-reduction methods, which can compromise acceptance quality by limiting token proposal capabilities, SlimSpec achieves a 4-5x reduction in LM-head costs in experiments, such as EAGLE-3, while preserving competitive acceptance quality. This approach is particularly beneficial for production environments with strict throughput and latency requirements, as it balances speed and token acceptance effectively. SlimSpec avoids the limitations of static and dynamic vocabulary truncation methods, which either cap acceptance rates or introduce additional inference-time complexities. The architecture has shown superior performance across various models and benchmarks, making it a practical choice for improving end-to-end speculative decoding throughput in applications like coding assistants and enterprise copilots. SlimSpec has been submitted to NeurIPS, and the preprint is available on ArXiv, offering production teams a robust solution for optimizing their speculative decoding workflows.

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