Summary of Speculative Streaming: Fast Llm Inference Without Auxiliary Models, by Nikhil Bhendawade et al.
Speculative Streaming: Fast LLM Inference without Auxiliary Models
by Nikhil Bhendawade, Irina Belousova, Qichen Fu, Henry Mason, Mohammad Rastegari, Mahyar Najibi
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 This research proposes a novel method called Speculative Streaming to accelerate the inference of large language models. The approach fuses drafting into the target model by altering the fine-tuning objective from predicting individual tokens to predicting future n-grams. This technique, which is based on speculative decoding, achieves speed-ups of 1.8-3.1X in various tasks such as summarization, structured queries, and meaning representation without compromising generation quality. Furthermore, Speculative Streaming is parameter-efficient, using approximately 10000 times fewer extra parameters than Medusa-style architectures, making it suitable for resource-constrained devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Speculative decoding helps large language models process information faster. However, this technique often requires fine-tuning two models together to work well. This can be complicated and slow. Researchers propose a new way to speed up this process called Speculative Streaming. Instead of using two separate models, it combines the drafting into the main model. This makes the process faster, with improvements ranging from 1.8 to 3.1 times in different tasks like summarization or searching for specific information. The best part is that it doesn’t sacrifice quality and uses much fewer extra resources. |
Keywords
* Artificial intelligence * Fine tuning * Inference * Parameter efficient * Summarization
