Summary of Inf-mllm: Efficient Streaming Inference Of Multimodal Large Language Models on a Single Gpu, by Zhenyu Ning et al.
Inf-MLLM: Efficient Streaming Inference of Multimodal Large Language Models on a Single GPU
by Zhenyu Ning, Jieru Zhao, Qihao Jin, Wenchao Ding, Minyi Guo
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
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 The paper introduces Inf-MLLM, an efficient inference framework for Multimodal Large Language Models (MLLMs) that enables streaming inference on a single GPU with infinite context. MLLMs are widely used in applications like GPT-4o, autonomous driving, and robotics, but their multimodal comprehensive ability is limited by the need to cache massive Key and Value states (KV cache), introducing high latency and excessive memory consumption. Inf-MLLM addresses this challenge by maintaining a size-constrained KV cache using attention saddles, a newly discovered pattern in both LLMs and MLLMs. This framework also proposes attention bias, which enables MLLMs to capture long-term dependencies. Experiments show that Inf-MLLM achieves stable performance on long texts and multi-round conversations, outperforming existing methods like StreamingLLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Inf-MLLM is a new way to make Multimodal Large Language Models work better with lots of information. These models are good at understanding many types of data, but they get slow when there’s too much context to remember. The Inf-MLLM system helps by being more efficient and using attention patterns to keep track of important information. This makes it possible for these models to work well even when there’s a lot of text or video to analyze. |
Keywords
» Artificial intelligence » Attention » Gpt » Inference
