Scaling Laws for Speculative Decoding

TL;DR

This paper establishes scaling laws for speculative decoding in large language models, enabling more efficient inference by coordinating model capacity, batch size, and training data, leading to significant throughput improvements.

Contribution

It introduces Log-linear Scaling Laws for speculative decoding acceptance rates and develops Scylla, a multi-dimensional scaling method for popular LLMs, validated through empirical experiments.

Findings

Scylla achieves 1.5-2.2x higher acceptance rate than EAGLE2.

Decoding throughput is doubled compared to EAGLE2 in industrial settings.

Scaling laws accurately predict decoding efficiency across different model sizes.

Abstract

The escalating demand for efficient decoding in large language models (LLMs) is particularly critical for reasoning-intensive architectures like OpenAI-o3 and DeepSeek-R1, which depend on extended chain-of-thought reasoning. This study investigates speculative decoding techniques through dense LLM architectures to establish foundational insights for accelerating reasoning tasks. While speculative decoding methods leveraging parallel draft-verification cycles have emerged as promising acceleration techniques, the scaling laws governing decoding efficiency remain under-explored compared to conventional backbone LLMs developed through Pretraining->SFT->RLHF training paradigms. In this work, we discover Log-linear Scaling Laws (Theorem 1.1, 1.2 and 1.3) governing draft model acceptance rate (or decoding speed) across three dimensions: pretraining token volume, draft model capacity, and decoding batch size. Building on these laws, we achieve Scylla, which coordinates multi-dimensional scaling for popular LLMs (Llama2/3, Qwen2.5). Empirical validation shows Scylla achieves 1.5-2.2 higher acceptance rate than EAGLE2 and 0.3 higher than EAGLE3 at temperature T = 0, with peak performance gains on summarization and QA tasks (Figure 2). Industrial inference engine deployments demonstrate 2X decoding throughput improvements over EAGLE2 (Table 5), validating the transformative potential of systematic scaling for efficient LLM inference. Code will be released later.