Running Large Language Models in the Browser: WebLLM and Transformers.js - Part 2

1. INTRODUCTION

In recent years, the dominance of large language models (LLMs) has shifted how developers think about intelligent software systems. These models, based on the Transformer architecture, have powered applications ranging from code generation and summarization to virtual assistants and interactive search engines. Traditionally, LLMs have relied on server-side execution due to their intensive compute requirements. But with innovations in WebAssembly, WebGPU, and efficient quantized model formats, it is now possible to run these models directly in the browser.

This transformation gives rise to client-side inference technologies like WebLLM and Transformers.js from HuggingFace. They allow developers to use language models in JavaScript environments, which means private, offline, and cross-platform execution—without sending any data to the cloud. This opens up opportunities for privacy-preserving chatbots, edge AI, offline dev tools, and more.

Before we dive into the specifics of each framework, we must briefly consider the inherent limitations and capabilities of browser-based environments that made this shift feasible.

2. BROWSER CONSTRAINTS AND THE RISE OF WEBGPU

Browsers historically were not designed for machine learning workloads. JavaScript is single-threaded by design, memory allocation is sandboxed, and direct access to hardware is restricted. Running LLMs in this environment seemed absurd just a few years ago. However, three major advances made it possible.

The first enabling technology is WebAssembly (WASM), which provides a low-level binary format that runs at near-native speeds within the browser sandbox. It allows languages like C++ and Rust to be compiled for the web.

The second breakthrough is WebGPU, the successor to WebGL, which allows access to the graphics hardware for general-purpose computation, not just rendering. It offers compute shaders that are essential for accelerating tensor operations.

The third key is model optimization through quantization, pruning, and efficient kernel scheduling, often using compiler stacks like Apache TVM, which emits WebAssembly and WebGPU-compatible code.

With this foundation in place, let us explore how WebLLM leverages this stack.

3. WHAT IS WEBLLM?

WebLLM is an open-source project built by the MLC (Machine Learning Compilation) team that allows developers to run quantized LLMs such as Vicuna, RedPajama, or Mistral directly in the browser. It is powered by the TVM Unity compiler and emits WebGPU or WASM code depending on the browser capabilities.

WebLLM loads a quantized model (typically in GGML format), initializes tokenizers in JavaScript, and runs the transformer inference loop in the browser itself. Models are downloaded and cached locally. It supports various Transformer architectures including LLaMA, Mistral, and more.

Here is a minimal example of how one would run WebLLM in a JavaScript app:

EXAMPLE: Using WebLLM in a browser environment

First, you must include the WebLLM loader in your HTML:

<script src="https://mlc.ai/web-llm/webllm.min.js"></script>

Next, in your JavaScript code, initialize and use the model:

async function main() {

    const webLLM = await webllm.CreateChatModule();

    await webLLM.reload("RedPajama-INCITE-Chat-3B-v1-q4f16_1");

    const response = await webLLM.chat.completions.create({

        messages: [{ role: "user", content: "Explain WebLLM in simple terms." }],

        temperature: 0.7,

    });

    console.log("AI:", response.choices[0].message.content);

}

main();

This example does the following:

First, it dynamically loads the chat module which includes both tokenizer and model runner. Then, it downloads a quantized version of the RedPajama model and loads it in browser memory. After that, it sends a prompt and gets the model’s response as JSON.

Internally, the model runs on WebGPU if available, falling back to WASM when needed. Memory usage is minimized by quantization, and caching ensures repeated calls do not reload model weights unnecessarily.

Next, we explore HuggingFace’s Transformers.js.

4. WHAT IS TRANSFORMERS.JS?

Transformers.js is a JavaScript runtime library created by HuggingFace that brings pre-trained transformer models into the browser using ONNX format and TensorFlow.js/WebGL or WebAssembly backends. It is designed to work seamlessly with HuggingFace Hub, allowing you to load models like BERT, DistilBERT, GPT2, and Whisper directly in web apps.

Unlike WebLLM, which compiles GGML quantized models to WebGPU kernels, Transformers.js uses pre-converted ONNX models and runs them through a JavaScript-based ONNX runtime. While it is not yet suitable for very large models like LLaMA-13B, it is excellent for small to medium models in classification, tokenization, translation, and summarization.

Here is a code sample:

EXAMPLE: Using Transformers.js to run a BERT model in browser

import { pipeline } from '@xenova/transformers';

async function runSentimentAnalysis() {

    const classifier = await pipeline('sentiment-analysis');

    const result = await classifier("WebLLM and Transformers.js are amazing tools!");

    console.log(result);

}

runSentimentAnalysis();

This snippet uses the pipeline abstraction from Transformers.js, which mimics the Python-based HuggingFace Transformers API. The first call downloads and initializes a sentiment analysis model (typically DistilBERT or similar). The inference is then done fully client-side.

One powerful feature of Transformers.js is that you do not need to worry about tokenizer details or backend execution. All model weights are served as pre-hosted assets on HuggingFace’s CDN, and the runtime chooses the best available backend (WebGL, WASM, or plain JS).

5. COMPARING WEBLLM AND TRANSFORMERS.JS

Both WebLLM and Transformers.js represent significant progress in bringing intelligent language models to the browser. However, they cater to different parts of the use case spectrum, driven by architectural choices and tradeoffs in model complexity, runtime performance, and browser compatibility.

WebLLM focuses on running large decoder-only models like LLaMA, Vicuna, or Mistral. These models are typically quantized (e.g., 4-bit or 8-bit formats) and compiled using the Apache TVM stack into WebGPU shaders or WebAssembly. WebLLM prioritizes inference efficiency, minimal memory footprint, and the ability to handle longer context windows. Due to the nature of these models, WebLLM is best suited for applications where full generative text capabilities are required, such as chatbots, code assistants, or offline question answering systems.

Transformers.js, in contrast, targets smaller encoder or encoder-decoder models, especially those pre-converted to the ONNX format and hosted on HuggingFace’s model hub. The architecture is more general-purpose and supports tasks like text classification, named entity recognition, translation, and summarization, where model sizes and latency demands are moderate. The setup is easier, and the pipelines are abstracted in a way that allows most developers to avoid dealing with low-level tensor operations.

The performance differences stem mainly from the backend. WebLLM uses WebGPU, which maps computation directly to the user’s GPU (on supported browsers such as Chromium and Safari Technology Preview). Transformers.js relies on WebGL or WebAssembly backends, which are older and often slower. This means WebLLM generally outperforms Transformers.js when running large models, while Transformers.js offers quicker start-up and better support across a wider range of devices including mobile Safari.

6. PRACTICAL CONSIDERATIONS

Running LLMs in the browser is not without constraints. Unlike server-based deployments where GPU RAM can range from 16GB to 80GB, browsers are limited to a fraction of main memory and are restricted by WebAssembly’s 4GB heap limitation unless using WebAssembly 64-bit memory extensions (still experimental). For this reason, model quantization becomes essential, often reducing model sizes from several gigabytes to under 1GB while preserving acceptable accuracy.

Latency is another concern. On devices without WebGPU support, inference might fall back to CPU-bound WebAssembly, making text generation painfully slow. For example, a 7B model like Vicuna-7B running in pure WASM can take several seconds per token. By contrast, with WebGPU, token latency drops to sub-300ms on modern laptops.

Moreover, model loading time and bandwidth usage are critical in browser settings. While server-hosted APIs can hide latency behind request queues, client-side models must be downloaded in full before use. This can involve hundreds of megabytes, depending on the model.

Another limitation is prompt size. Browsers often cap memory usage per tab, so very long prompts or context windows (like 8k+ tokens) may be truncated or fail. Developers must balance token count, quantization level, and memory lifecycle handling in JavaScript.

Despite these issues, the benefit of privacy-preserving, offline, customizable inference is extremely compelling—especially for applications in education, assistive technologies, developer tools, and personal productivity software.

7. ADVANCED USE CASES

Once LLMs run locally in the browser, several creative architectures become possible. For instance, you can use the browser-based LLM as a co-processor for your frontend UI. It can pre-analyze user queries, detect intent, or generate structured data from input—all without any server calls.

One interesting setup is hybrid inference, where smaller prompts or classification tasks are handled locally using Transformers.js, and fallback or complex completions are routed to WebLLM (or even a cloud API) via a dynamic planner. This approach keeps fast interactions client-side while leveraging stronger models only when necessary.

Another powerful technique is prompt chaining inside the browser. You can simulate agentic behavior by generating a plan with one LLM invocation, then using JavaScript logic to feed individual steps back into the model in sequence. This creates multi-step, context-aware flows without ever leaving the local device.

Some developers even load WebLLM in Electron or Tauri apps, allowing for completely offline, native desktop AI tools that embed language model inference using web technologies.

8. SUMMARY AND OUTLOOK

WebLLM and Transformers.js represent two pillars of a larger shift toward client-side artificial intelligence. Powered by WebAssembly, WebGPU, and efficient model formats, these frameworks bring powerful transformer models into your browser. WebLLM aims for large-scale generative modeling with full token streaming and efficient GPU use, while Transformers.js brings ONNX-based models into friendly pipelines for classification and translation.

The key takeaway is that developers now have a choice. Whether you are building privacy-respecting AI tools, fast-on-device analytics, or educational apps that must run offline, you no longer need to compromise on intelligence due to a lack of server-side compute. As WebGPU matures and 4-bit quantization becomes more widespread, we can expect LLaMA 3-sized models to become truly browser-native—opening up a new frontier for frontend engineering.