[Submitted on 20 Dec 2024 (
), last revised 13 Apr 2026 (this version, v2)]
Title:WebLLM: A High-Performance In-Browser LLM Inference Engine
Authors:Charlie F. Ruan
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View a PDF of the paper titled WebLLM: A High-Performance In-Browser LLM Inference Engine, by Charlie F. Ruan and 13 other authors
Abstract:Advancements in large language models (LLMs) have unlocked remarkable capabilities. While deploying these models typically requires server-grade GPUs and cloud-based inference, the recent emergence of smaller open-source models and increasingly powerful consumer devices have made on-device deployment practical. The web browser as a platform for on-device deployment is universally accessible, provides a natural agentic environment, and conveniently abstracts out the different backends from diverse device vendors. To address this opportunity, we introduce WebLLM, an open-source JavaScript framework that enables high-performance LLM inference entirely within web browsers. WebLLM provides an OpenAI-style API for seamless integration into web applications, and leverages WebGPU for efficient local GPU acceleration and WebAssembly for performant CPU computation. With machine learning compilers MLC-LLM and Apache TVM, WebLLM leverages optimized WebGPU kernels, overcoming the absence of performant WebGPU kernel libraries. Evaluations show that WebLLM can retain up to 80% native performance on the same device, with room to further close the gap. WebLLM paves the way for universally accessible, privacy-preserving, personalized, and locally powered LLM applications in web browsers. The code is available at: this https URL.
Submission history
From: Charlie Ruan [
]
Fri, 20 Dec 2024 11:24:13 UTC (278 KB)
[v2]
Mon, 13 Apr 2026 04:55:03 UTC (149 KB)