Summary of Efficient Llm Inference with I/o-aware Partial Kv Cache Recomputation, by Chaoyi Jiang et al.
Efficient LLM Inference with I/O-Aware Partial KV Cache Recomputation
by Chaoyi Jiang, Lei Gao, Hossein Entezari Zarch, Murali Annavaram
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
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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 This paper proposes an efficient approach for inference in Large Language Models (LLMs) by reducing the computational overhead of auto-regressive decoding using Key-Value (KV) caching. The proposed method offloads KV cache to CPU memory, alleviating GPU memory pressure and shifting the bottleneck to the limited bandwidth of the PCIe connection between the CPU and GPU. To address this issue, the paper introduces a novel method that overlaps GPU recomputation with data transfer, minimizing idle GPU time and maximizing inference performance. The proposed approach is fully automated by integrating a profiler module, scheduler module, and runtime module to optimize execution workloads. Experimental results show significant improvements in latency (up to 35.8% lower) and throughput (up to 46.2% higher) compared to state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models faster and more efficient. Language models are like super smart computers that can understand and generate human-like text. But they use up a lot of computer power, which makes them slow. To solve this problem, the researchers came up with a new way to store information in memory so that computers don’t have to do as much extra work. This method saves time and makes language models run faster. The paper also shows how it can be used to make language models run even faster by moving some of the work from one computer part (the GPU) to another (the CPU). Overall, this is an important step towards making language models more useful for us. |
Keywords
* Artificial intelligence * Inference
