English

Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI

Distributed, Parallel, and Cluster Computing 2025-07-17 v1 Machine Learning

Abstract

Inference is now the dominant AI workload, yet existing systems force trade-offs between latency, throughput, and cost. Arctic Inference, an open-source vLLM plugin from Snowflake AI Research, introduces Shift Parallelism, a dynamic parallelism strategy that adapts to real-world traffic while integrating speculative decoding, SwiftKV compute reduction, and optimized embedding inference. It achieves up to 3.4 times faster request completion, 1.75 times faster generation, and 1.6M tokens/sec per GPU for embeddings, outperforming both latency- and throughput-optimized deployments. Already powering Snowflake Cortex AI, Arctic Inference delivers state-of-the-art, cost-effective inference for enterprise AI and is now available to the community.

Keywords

Cite

@article{arxiv.2507.11830,
  title  = {Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI},
  author = {Samyam Rajbhandari and Mert Hidayetoglu and Aurick Qiao and Ye Wang and Juncheng Yang and Jeff Rasley and Michael Wyatt and Yuxiong He},
  journal= {arXiv preprint arXiv:2507.11830},
  year   = {2025}
}
R2 v1 2026-07-01T04:03:27.070Z