• Parallel Track Transformers: Enabling Fast GPU Inference with Reduced Synchronization Parallel Track Transformers: Enabling Fast GPU Inference with Reduced Synchronization AuthorsChong Wang**, Nan Du**, Tom Gunter**, Tao Lei, Kulin Seth, Senyu Tong**, Jianyu Wang, Guoli Yin, Xiyou Zhou, Kelvin Zou**, Ruoming Pang** View publication Copy Bibtex 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. • ** Work done while at Apple Related readings and updates.

Article Summaries:

  • Parallel Track Transformers: Enabling Fast GPU Inference with Reduced Synchronization

Researchers introduce the Parallel Track (PT) Transformer, a new architecture that restructures transformer computations to cut cross‑GPU synchronization by up to 16× compared with conventional tensor parallelism. PT maintains model quality while dramatically improving inference efficiency on large‑scale language models. When integrated into popular serving stacks-Tensor‑RT‑LLM and vLLM-the approach yields 15-30 % faster time‑to‑first‑token, 2-12 % lower per‑token latency, and up to 31.9 % higher throughput. The work addresses the growing communication bottleneck in multi‑GPU LLM inference, offering a scalable solution for low‑latency, high‑throughput deployment.

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