mlsanityData to 14 Oct 2026

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

arXiv 2507.11830 ↗First posted 16 Jul 2025cs.DC cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 16 Jul 2025.

Builds on

8 of 19 references.

References · 8

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  5. 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,141
  6. 2024EAGLE: Speculative Sampling Requires Rethinking Feature Uncertaintycited by 578
  7. 2024Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Modelscited by 61
  8. 2024SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformationcited by 19

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026FLYING SERVING: On-the-Fly Parallelism Switching for Large Language Model Servingcited by 6
  2. 2025Shift Parallelism: Low-Latency, High-Throughput LLM Inference for Dynamic Workloadscited by 5

Similar, not cited

Close in wording, no citation either way

  1. 2026Characterizing Parallelism Strategies in LLM Inference: Fundamental Compute-Communication Trade-offs
  2. 2026InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents
  3. 2026Separating Intelligence from Inference: A Standard for Edge-Native AI Computing ↗
  4. 2026Understanding Inference Scaling for LLMs: Bottlenecks, Trade-offs, and Performance Principles
  5. 2024Distributed Speculative Inference (DSI): Speculation Parallelism for Provably Faster Lossless Language Model Inference
  6. 2024Inference Acceleration for Large Language Models on CPUs
  7. 2026Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
  8. 2026Evaluating Inference Compute for Generative AI: A Framework for Enterprise Workloads