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arXiv · 2507.11830

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

Samyam Rajbhandari·Mert Hidayetoglu·Aurick Qiao·Ye Wang·Juncheng Yang·Jeff Rasley·Michael Wyatt·Yuxiong He
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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.

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Samyam Rajbhandari, Mert Hidayetoglu, Aurick Qiao, Ye Wang, Juncheng Yang, Jeff Rasley, Michael Wyatt, Yuxiong He. 2025-07-16. Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI. https://arxiv.org/abs/2507.11830

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