Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
36 tokens/sec
GPT-4o
12 tokens/sec
Gemini 2.5 Pro Pro
37 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
4 tokens/sec
DeepSeek R1 via Azure Pro
33 tokens/sec
2000 character limit reached

Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies (2502.05202v3)

Published 31 Jan 2025 in cs.CL, cs.AI, and cs.LG

Abstract: Accelerating the inference of LLMs is a critical challenge in generative AI. Speculative decoding (SD) methods offer substantial efficiency gains by generating multiple tokens using a single target forward pass. However, existing SD approaches require the drafter and target models to share the same vocabulary, thus limiting the pool of possible drafters, often necessitating the training of a drafter from scratch. We present three new SD methods that remove this shared-vocabulary constraint. All three methods preserve the target distribution (i.e., they are lossless) and work with off-the-shelf models without requiring additional training or modifications. Empirically, on summarization, programming, and long-context tasks, our algorithms demonstrate significant speedups of up to 2.8x over standard autoregressive decoding. By enabling any off-the-shelf model to serve as a drafter and requiring no retraining, this work substantially broadens the applicability of the SD framework in practice.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Youtube Logo Streamline Icon: https://streamlinehq.com