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

TL;DR

Arctic Inference introduces Shift Parallelism, a dynamic and efficient open-source system for enterprise AI inference that significantly improves speed and cost-effectiveness by integrating innovative parallelism and decoding strategies.

Contribution

The paper presents Shift Parallelism, a novel dynamic parallelism strategy for AI inference that adapts to traffic and enhances performance and efficiency.

Findings

Up to 3.4x faster request completion

1.75x faster generation

1.6M tokens/sec per GPU for embeddings

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.