Summary of Challenges in Deploying Long-context Transformers: a Theoretical Peak Performance Analysis, by Yao Fu
Challenges in Deploying Long-Context Transformers: A Theoretical Peak Performance Analysis
by Yao Fu
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 paper presents a concurrent programming framework to analyze the efficiency challenges in serving multiple requests with long-context transformers under limited GPU high-bandwidth memory (HBM) regime. It highlights the single source of additional computational costs: the large size of the KV cache. The authors use a 34B GPT-3.5 level model as an example, illustrating four deployment challenges: prefilling long inputs takes longer compute time and GPU memory; concurrent user limits are restricted due to KV cache residing on HBM; decoding latency increases with repeated KV cache reading; and context switching latency occurs when KV cache memory overflows. The framework is used to analyze existing works and identify possibilities for building end-to-end systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Long-context generative models are powerful tools in AI applications, but deploying them can be expensive. The problem is especially challenging starting from 2024. This paper introduces a new framework to understand the efficiency challenges of serving multiple long-context requests on limited GPU memory. It shows that the main issue is the large size of the KV cache. The authors use an example model and illustrate four problems: taking longer to prefill long inputs, limiting concurrent users due to KV cache size, increasing latency during decoding, and high context switching latency when memory overflows. |
Keywords
» Artificial intelligence » Gpt
