Spec-LLaVA: Accelerating Vision-Language Models with Dynamic Tree-Based Speculative Decoding

• URL: http://arxiv.org/abs/2509.11961v1
• Date: Mon, 15 Sep 2025 14:16:51 GMT
• Title: Spec-LLaVA: Accelerating Vision-Language Models with Dynamic Tree-Based Speculative Decoding
• Authors: Mingxiao Huo, Jiayi Zhang, Hewei Wang, Jinfeng Xu, Zheyu Chen, Huilin Tai, Yijun Chen,
• Abstract summary: Spec-LLaVA is a system that applies speculative decoding to accelerate Vision-Language Models without sacrificing output quality.<n>On MS out-of-domain images, Spec-LLaVA achieves up to 3.28$times$ faster decoding on LLaVA-1.5 (7B, 13B) with no loss in generation quality.
• Score: 14.571291239004225
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) enable powerful multimodal reasoning but suffer from slow autoregressive inference, limiting their deployment in real-time applications. We introduce Spec-LLaVA, a system that applies speculative decoding to accelerate VLMs without sacrificing output quality. Spec-LLaVA pairs a lightweight draft VLM with a large target model: the draft speculates future tokens, which the target verifies in parallel, allowing multiple tokens to be generated per step. To maximize efficiency, we design a dynamic tree-based verification algorithm that adaptively expands and prunes speculative branches using draft model confidence. On MS COCO out-of-domain images, Spec-LLaVA achieves up to 3.28$\times$ faster decoding on LLaVA-1.5 (7B, 13B) with no loss in generation quality. This work presents a lossless acceleration framework for VLMs using dynamic tree-structured speculative decoding, opening a path toward practical real-time multimodal assistants. Importantly, the lightweight draft model design makes the framework amenable to resource-constrained or on-device deployment settings.

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