# 3-Model Speculative Decoding

arXiv: https://arxiv.org/abs/2510.12966 · HTML: https://mlsanity.com/paper/2510.12966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Speculative decoding, one of 12 directions in Efficient LLM inference and serving.
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Placed in

- Efficient LLM inference and serving / [Speculative decoding](https://mlsanity.com/inference/speculative-decoding/)

## Builds on (most cited first, 10 of 10)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [Accelerating Large Language Model Decoding with Speculative Sampling](https://mlsanity.com/paper/2302.01318/) (2023)
- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty](https://mlsanity.com/paper/2401.15077/) (2024)
- [Break the Sequential Dependency of LLM Inference Using Lookahead Decoding](https://mlsanity.com/paper/2402.02057/) (2024)
- [SpecTr: Fast Speculative Decoding via Optimal Transport](https://mlsanity.com/paper/2310.15141/) (2023)
- [Cascade Speculative Drafting for Even Faster LLM Inference](https://mlsanity.com/paper/2312.11462/) (2023)
- [Fuzzy Speculative Decoding for a Tunable Accuracy-Runtime Tradeoff](https://mlsanity.com/paper/2502.20704/) (2025)

## Abstract (arXiv)

Speculative Decoding (SD) accelerates inference in large language models by using a smaller draft model to propose tokens, which are then verified by a larger target model. However, the throughput gains of SD are fundamentally limited by a trade-off between draft model size and token acceptance: smaller draft models generate tokens more quickly but exhibit greater divergence from the target model, resulting in lower acceptance rates and reduced speedups. We introduce Pyramid Speculative Decoding (PyramidSD), an extension of SD that inserts an intermediate qualifier model between the draft and target to bridge the distributional gap in output predictions, allowing smaller model to be used for drafting. This hierarchical decoding strategy improves alignment across models, enabling higher acceptance rates and allowing the use of significantly smaller draft models without sacrificing overall performance. PyramidSD builds on fuzzy acceptance criteria to support relaxed divergence thresholds at each stage, improving throughput. In experiments, PyramidSD achieves up to 1.91x generation speed over standard SD, reaching 124 tokens per second on a consumer GPU (RTX 4090). In small-memory settings with a 1B-parameter draft model and an 8B target model, PyramidSD minimally trades target model quality for improved throughput. Overall, PyramidSD offers a practical approach to enhancing speculative decoding efficiency and can be readily applied to existing inference pipelines.