Parallel Track Transformers: Enabling Fast GPU Inference with Reduced Synchronization

TL;DR

This paper introduces the Parallel Track Transformer, a new architecture that significantly reduces synchronization in GPU inference of large language models, leading to faster and more scalable deployment.

Contribution

The paper presents the PT Transformer, a novel approach that restructures computation to minimize cross-GPU dependencies and synchronization, improving efficiency without sacrificing model quality.

Findings

Up to 16x reduction in synchronization operations.

15-30% faster time to first token.

Up to 31.90% increased throughput.

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.