Summary of Webllm: a High-performance In-browser Llm Inference Engine, by Charlie F. Ruan et al.
WebLLM: A High-Performance In-Browser LLM Inference Engine
by Charlie F. Ruan, Yucheng Qin, Xun Zhou, Ruihang Lai, Hongyi Jin, Yixin Dong, Bohan Hou, Meng-Shiun Yu, Yiyan Zhai, Sudeep Agarwal, Hangrui Cao, Siyuan Feng, Tianqi Chen
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces WebLLM, an open-source JavaScript framework that enables high-performance large language model (LLM) inference within web browsers. The emergence of smaller LLMs and powerful consumer devices has made on-device deployment practical, but existing solutions require server-grade GPUs and cloud-based inference. WebLLM leverages OpenAI-style APIs for seamless integration into web applications, and uses WebGPU for efficient local GPU acceleration and WebAssembly for performant CPU computation. By leveraging optimized WebGPU kernels through machine learning compilers MLC-LLM and Apache TVM, WebLLM can retain up to 80% native performance on the same device, with room to further close the gap. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WebLLM is a new way to use powerful language models directly in web browsers. Right now, these models are usually used on big computers or in the cloud. This makes it hard for people to use them on their own devices. The WebLLM project wants to change that by creating a special kind of software that lets language models work well on regular computers and phones. This means you can use these powerful tools without needing a super-powerful computer or being connected to the internet. |
Keywords
* Artificial intelligence * Inference * Large language model * Machine learning
