Summary of Fast Inference For Augmented Large Language Models, by Rana Shahout et al.
Fast Inference for Augmented Large Language Models
by Rana Shahout, Cong Liang, Shiji Xin, Qianru Lao, Yong Cui, Minlan Yu, Michael Mitzenmacher
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Augmented Large Language Models (LLMs) enhance standalone LLMs by integrating external data sources through API calls. Efficient scheduling is crucial for maintaining low request completion times in interactive applications, directly impacting user engagement. Traditional size-based scheduling algorithms, such as Shortest Job First (SJF), become less effective when handling requests with API calls due to memory constraints. This paper proposes LAMPS, a novel LLM inference framework that minimizes request completion time through a unified scheduling approach considering total request length and handling strategies during API calls. Our approach ranks requests based on memory consumption over time, predicting the strategy that minimizes memory waste during API calls. We also propose starvation prevention techniques and optimizations to mitigate scheduling overhead. Evaluations on top of vLLM demonstrate improvements in end-to-end latency by 27%-85% and reductions in TTFT by 4%-96% compared to existing augmented-LLM systems, with even greater gains over vLLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making language models better by combining them with external data sources. This helps interactive applications like chatbots or language translation tools work faster and more efficiently. Right now, traditional scheduling methods don’t work well because they don’t consider how the model handles requests from these outside sources. The researchers propose a new approach called LAMPS that takes into account how much memory each request uses and when it’s being handled. This helps reduce the time it takes to complete requests and makes the system more efficient. They also came up with ways to prevent certain requests from getting stuck or delayed, which further improves performance. |
Keywords
» Artificial intelligence » Inference » Translation
