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

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, and 3D Gaussian Splatting compression running in the browser.

npm packagewith embedded WASM —npm install turboquant-wasm

Relaxed SIMD—@mulAdd

FMA maps tof32x4.relaxed_madd

SIMD-vectorizedQJL sign packing/unpacking and scalingTypeScript API—TurboQuant.init()

/encode()

/decode()

/dot()

Golden-value tests— byte-identical output with the reference Zig implementation

The WASM binary uses relaxed SIMD instructions:

| Runtime | Minimum Version | |---|---| | Chrome | 114+ | | Firefox | 128+ | | Safari | 18+ | | Node.js | 20+ |

import { TurboQuant } from "turboquant-wasm";
const tq = await TurboQuant.init({ dim: 1024, seed: 42 });
// Compress a vector (~4.5 bits/dim, ~6x compression)
const compressed = tq.encode(myFloat32Array);
// Decode back
const decoded = tq.decode(compressed);
// Fast dot product without decoding
const score = tq.dot(queryVector, compressed);
tq.destroy();

class TurboQuant {
static async init(config: { dim: number; seed: number }): Promise<TurboQuant>;
encode(vector: Float32Array): Uint8Array;
decode(compressed: Uint8Array): Float32Array;
dot(query: Float32Array, compressed: Uint8Array): number;
destroy(): void;
}

# Run tests
zig test -target aarch64-macos src/turboquant.zig
# Full npm build (zig -> wasm-opt -> base64 embed -> bun + tsc)
bun run build
# Build WASM only
bun run build:zig

Requires Zig 0.15.2 and Bun.

Encoding preserves inner products — verified by golden-value tests and distortion bounds:

MSEdecreases with dimension (unit vectors)Bits/dimis ~4.5 (payload only, excluding 22-byte header)Dot product preservation— mean absolute error < 1.0 for unit vectors at dim=128Bit-identicaloutput with botirk38/turboquant for same input + seed

• botirk38/turboquant — original Zig implementation
• TurboQuant paper (Google Research, ICLR 2026) — algorithm design

MIT