Parallel Track Transformers: Enabling Fast GPU Inference with Reduced Synchronization

2026-02-07

AI Summary

This work addresses the communication bottleneck caused by frequent inter-device synchronization in multi-GPU tensor-parallel inference, which limits scalability. The authors propose the Parallel Track Transformer architecture, featuring an innovative parallel-track computation structure and a novel task partitioning and scheduling strategy that substantially reduces cross-device dependencies while preserving model quality. Implemented within the TensorRT-LLM and vLLM frameworks, the approach achieves up to a 16-fold reduction in synchronization operations, decreases first-token latency by 15–30%, reduces per-token generation time by 2–12%, and improves throughput by as much as 31.9%.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Efficient large-scale inference of transformer-based large language models (LLMs) remains a fundamental systems challenge, frequently requiring multi-GPU parallelism to meet stringent latency and throughput targets. Conventional tensor parallelism decomposes matrix operations across devices but introduces substantial inter-GPU synchronization, leading to communication bottlenecks and degraded scalability. We propose the Parallel Track (PT) Transformer, a novel architectural paradigm that restructures computation to minimize cross-device dependencies. PT achieves up to a 16x reduction in synchronization operations relative to standard tensor parallelism, while maintaining competitive model quality in our experiments. We integrate PT into two widely adopted LLM serving stacks-Tensor-RT-LLM and vLLM-and report consistent improvements in serving efficiency, including up to 15-30% reduced time to first token, 2-12% reduced time per output token, and up to 31.90% increased throughput in both settings.

Problem

• large language models
• tensor parallelism
• GPU inference
• synchronization bottleneck
• scalability

Innovation

• Parallel Track Transformer
• tensor parallelism
• GPU synchronization
• LLM inference
• model serving