Summary of Infercept: Efficient Intercept Support For Augmented Large Language Model Inference, by Reyna Abhyankar et al.
InferCept: Efficient Intercept Support for Augmented Large Language Model Inference
by Reyna Abhyankar, Zijian He, Vikranth Srivatsa, Hao Zhang, Yiying Zhang
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) are being integrated with external tools and agents, enabling them to perform tasks beyond natural language processing. However, current LLM inference systems are designed for standalone models and do not effectively handle these integrations. In particular, they treat each interaction as the end of LLM generation, leading to unnecessary recomputation of already computed contexts that account for 37-40% of total model forwarding time. This paper presents InferCept, a novel framework for efficient LLM inference in augmented scenarios. InferCept minimizes GPU resource waste and dedicates saved memory to serve more requests, resulting in a significant improvement in serving throughput (1.6x-2x) and request completion rate (2x). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being used in new ways by combining them with other tools and systems. However, these combinations can make the LLMs less efficient than they could be. This paper introduces a new way to make LLMs more efficient when working with external tools and systems. The new approach, called InferCept, helps reduce wasted resources and allows for more requests to be handled at once. As a result, InferCept can process requests much faster (up to 2 times) than current methods. |
Keywords
* Artificial intelligence * Inference * Natural language processing
