Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI
Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI
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.
Samyam Rajbhandari、Mert Hidayetoglu、Aurick Qiao、Ye Wang、Juncheng Yang、Jeff Rasley、Michael Wyatt、Yuxiong He
计算技术、计算机技术
Samyam Rajbhandari,Mert Hidayetoglu,Aurick Qiao,Ye Wang,Juncheng Yang,Jeff Rasley,Michael Wyatt,Yuxiong He.Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI[EB/OL].(2025-07-16)[2025-08-05].https://arxiv.org/abs/2507.11830.点此复制
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