While JavaScript remains the reigning language for web UI rendering, high-throughput client-side computeβsuch as local browser AI inference, video encoding, and cryptographic verificationβhas completely shifted to Rust compiled to WebAssembly (WASM).
In 2026, running 1B+ parameter models directly inside the browser using WebGPU and WASM SIMD has become standard practice.
Execution Time (Lower is Better)
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β JavaScript (V8 Engine) : β β β β β β β β β β 1,420 ms β
β Rust WASM SIMD : β β 210 ms β
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Add the wasm-bindgen
dependency in your Cargo.toml
:
[package]
name = "wasm_ai_engine"
version = "0.1.0"
edition = "2021"
[lib]
crate-type = ["cdylib"]
[dependencies]
wasm-bindgen = "0.2"
Implement high-speed array processing in src/lib.rs
:
use wasm_bindgen::prelude::*;
#[wasm_bindgen]
pub fn process_tensor_data(inputs: &[f32], multiplier: f32) -> Vec<f32> {
inputs.iter().map(|&x| x * multiplier).collect()
}
#[wasm_bindgen]
pub fn compute_cosine_similarity(vec_a: &[f32], vec_b: &[f32]) -> f32 {
let dot_product: f32 = vec_a.iter().zip(vec_b.iter()).map(|(a, b)| a * b).sum();
let norm_a: f32 = vec_a.iter().map(|a| a * a).sum::<f32>().sqrt();
let norm_b: f32 = vec_b.iter().map(|b| b * b).sum::<f32>().sqrt();
if norm_a == 0.0 || norm_b == 0.0 {
return 0.0;
}
dot_product / (norm_a * norm_b)
}
Compile directly to WebAssembly:
wasm-pack build --target web
python
import init, { compute_cosine_similarity } from './pkg/wasm_ai_engine.js';
async function runVectorSearch() {
await init();
const vec1 = new Float32Array([0.12, 0.45, 0.98]);
const vec2 = new Float32Array([0.15, 0.42, 0.95]);
const similarity = compute_cosine_similarity(vec1, vec2);
console.log(`Calculated Vector Similarity (WASM): ${similarity.toFixed(4)}`);
}
runVectorSearch();
Lakshan Muruganandam is a software engineer and tech creator building high-performance dev tools, AI systems, and security tools.