Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI
This provides state-of-the-art, cost-effective inference for enterprise AI, though it appears incremental as it builds on existing systems like vLLM with new optimizations.
The paper tackles the problem of trade-offs between latency, throughput, and cost in AI inference workloads by introducing Arctic Inference with Shift Parallelism, achieving up to 3.4 times faster request completion, 1.75 times faster generation, and 1.6M tokens/sec per GPU for embeddings.
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.