TurboQuant-WASM – Google's vector quantization in the browser

In plain words

TurboQuant-WASM brings Google's vector quantization technology to web browsers through WebAssembly. It enables developers to compress and process high-dimensional vector data efficiently in client-side applications without server dependencies. The tool is designed for engineers building machine learning features in the browser, particularly those working with embeddings, similarity search, or neural network inference. Its main advantage is bringing complex quantization capabilities to web environments with minimal overhead.

written from the facts on this page · September 2026

From the sources

Experimental WASM + relaxed SIMD build of botirk38/turboquant for browsers and Node.js. Based on the paper "TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate" (Google Research, ICLR 2026). Live Demo — vector search, image similarity, 3D Gaussian Splatting compression, and a Gemma 4 E2B in-browser LLM whose KV cache is TurboQuant-compressed — all in the browser. Most of this repo is TurboQuant in WASM : a Zig → WASM build with relaxed SIMD that encodes / decodes / scores vectors on the CPU. That's what the turboquant-wasm npm package ships — the vector-search, image-similarity, and 3DGS demos all load this module and call into it. The Prompt → Diagram demo is different…from github.com

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