VoxServe: Streaming-Centric Serving System for Speech Language Models

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Authors: Keisuke Kamahori, Wei-Tzu Lee, Atindra Jha, Rohan Kadekodi, Stephanie F. Wang, Arvind Krishnamurthy, Baris Kasikci
Cited by: 0

Abstract

Deploying modern Speech Language Models (SpeechLMs) in streaming settings requires systems that provide low latency, high throughput, and strong guarantees of streamability. Existing systems fall short of supporting diverse models flexibly and efficiently. We present VoxServe, a unified serving system for SpeechLMs that optimizes streaming performance. VoxServe introduces a model-execution abstraction that decouples model architecture from system-level optimizations, thereby enabling support for diverse SpeechLM architectures within a single framework. Building on this abstraction, VoxServe implements streaming-aware scheduling and an asynchronous inference pipeline to improve end-to-end efficiency. Evaluations across multiple modern SpeechLMs show that VoxServe achieves 10-20x higher throughput than existing implementations at comparable latency while maintaining high streaming viability. The code of VoxServe is available at https://github.com/vox-serve/vox-serve.

Fields of Study & Subjects

Speech Recognition and Synthesis Advanced Neural Network Applications Parallel Computing and Optimization Techniques

Publication details

Journal / source
arXiv (Cornell University)
Publication date
30 January 2026
Document type
Preprint