Summary of Blendserve: Optimizing Offline Inference For Auto-regressive Large Models with Resource-aware Batching, by Yilong Zhao et al.
BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching
by Yilong Zhao, Shuo Yang, Kan Zhu, Lianmin Zheng, Baris Kasikci, Yang Zhou, Jiarong Xing, Ion Stoica
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed system, BlendServe, aims to optimize offline batch inference for latency-insensitive applications by combining resource overlapping and prefix sharing techniques. By reordering and overlapping requests with varied resource demands, BlendServe achieves a throughput boost of up to 1.44 times compared to industry standards like vLLM and SGLang. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline batch inference is becoming more popular for certain types of tasks that don’t require immediate results. This approach can be faster and cheaper than traditional methods. However, it also means that requests can be quite different from each other in terms of how much computer power or memory they need. To make the most of this, a system like BlendServe is needed to arrange these requests in a way that uses all available resources efficiently. This system works by rearranging and combining requests so that they use the right amount of computer power and memory for each one, while also making sure that similar parts of different requests are grouped together. As a result, it can process tasks up to 44% faster than current methods. |
Keywords
» Artificial intelligence » Inference
