pyturboquant 0.0.3

High-performance implementation of Google's TurboQuant: online vector quantization with near-optimal distortion rates

pyturboquant

A GPU-accelerated Python implementation of Google's TurboQuant: Online Vector Quantization with Near-Optimal Distortion Rate (arXiv:2504.19874, Google Research blog).

pyturboquant provides data-oblivious vector quantization that achieves near-Shannon-optimal distortion with zero indexing time -- no codebook training, no k-means, no data passes. Vectors are quantized independently using random rotations and precomputed Lloyd-Max codebooks, making it ideal for online settings and massive-scale nearest neighbor search.

Context

TurboQuant was introduced by Google Research in 2025 and has since been widely adopted by the community to compress the Key-Value (KV) cache of long-context models at inference time -- with Google's Gemma 4 (released April 2026) a particularly popular target through runtimes like MLX, llama.cpp, and inferrs. TurboQuant is not baked into Gemma 4's pretrained weights; it is applied online during the forward pass as a post-training optimization.

pyturboquant brings the same algorithm to the retrieval side of the modern LLM stack: compressing sentence-embedding vectors for memory-efficient RAG pipelines and high-throughput approximate nearest neighbor search. The hot path (KV cache during generation) and the cold path (embedding stores for retrieval) share the same core primitive -- a data-oblivious online quantizer -- and this library focuses on making that primitive usable for the latter.

Use Case: On-Premise RAG

Run an open-source embedding model (BGE, Gemma embeddings, nomic-embed, etc.) locally and store the resulting vectors with pyturboquant -- no data leaves your machine or VPC. At 4 bits per coordinate with the inner-product quantizer, a 10 M chunk BGE-base corpus fits in ~4 GB of RAM instead of ~31 GB, small enough to colocate with the embedding model on a single workstation GPU or an air-gapped enterprise server. There is no codebook training and no periodic reindexing as the corpus grows.

Per-vector storage cost (bytes) for the inner-product quantizer at bits=b is d*b/8 + 8, versus 4*d for fp32 and 2*d for fp16:

Embedding model	d	fp32	fp16	b=4	b=3	b=2
all-MiniLM-L6-v2	384	1 536	768	200	152	104
BGE-base / Gemma embeddings	768	3 072	1 536	392	296	200
BGE-large	1 024	4 096	2 048	520	392	264
OpenAI text-embedding-3-large	3 072	12 288	6 144	1 544	1 160	776

Scope notes:

• pyturboquant compresses the embedding vector store, not the embedding model itself. VRAM needed to run the embedding model is unchanged; use model-level quantization (AWQ, bitsandbytes, GGUF) for that.
• Search compute is currently O(n) per query (dense asymmetric matmul). Sub-linear search via IVF partitioning is on the roadmap (v0.5.0).
• For small corpora (< 100 k chunks) the document payload typically dominates; pyturboquant's savings become material at 1 M chunks and transformative at 10 M+.

Features

• MSE-Optimal Quantizer (Algorithm 1) -- per-coordinate scalar quantization after random rotation, approaching the Shannon lower bound at 1-8 bits
• Inner Product Quantizer (Algorithm 2) -- two-stage MSE + 1-bit QJL residual for unbiased inner product estimation
• Zero-indexing-time ANN search -- TurboQuantIndex with FAISS-like .add() / .search() API, asymmetric distance computation, save/load persistence; no training step, no data passes
• Truly online -- stream new documents in at any time without rebuilding or reindexing
• LangChain VectorStore -- drop-in TurboQuantVectorStore for low-RAM RAG pipelines
• Pure PyTorch -- no custom C++ extensions; runs on CPU and CUDA out of the box
• Deterministic -- seed-based rotation matrices and QJL projections for full reproducibility

How it compares to FAISS, ScaNN, and HNSW

	Training required	Online ingestion	Distortion guarantee
FAISS IVF/PQ	yes (k-means on a sample)	rebuild on drift	empirical
ScaNN	yes (learned quantizer)	rebuild on drift	empirical
HNSW	no	yes (graph mutation)	none
pyturboquant	no	yes (independent per vector)	within ~2.7x of Shannon bound

Installation

# Core library (torch only)
pip install pyturboquant

# With LangChain RAG support
pip install pyturboquant[langchain]

# Development (tests, linting, codebook generation)
pip install pyturboquant[dev]

# Everything
pip install pyturboquant[all]

Requires Python >= 3.12 and PyTorch >= 2.4.

Quick Start

Pure PyTorch Building Blocks

import torch
from pyturboquant.core import (
    mse_quantize, mse_dequantize,
    ip_quantize, estimate_inner_product,
    random_rotate, random_rotate_inverse,
)

x = torch.randn(1000, 256)

# MSE-optimal quantization (Algorithm 1)
qt = mse_quantize(x, bits=3, seed=42)
x_hat = mse_dequantize(qt)
mse = ((x - x_hat) ** 2).sum(dim=-1).mean()
print(f"Normalized MSE: {mse / (x ** 2).sum(dim=-1).mean():.4f}")  # ~0.034

# Inner-product-preserving quantization (Algorithm 2)
y = torch.randn(1000, 256)
qt_ip = ip_quantize(x, bits=4, seed=42)
ip_est = estimate_inner_product(qt_ip, y)
ip_true = (x * y).sum(dim=-1)
print(f"IP error: {(ip_est - ip_true).abs().mean():.4f}")

Nearest Neighbor Search

import torch
from pyturboquant.search import TurboQuantIndex

# Build index -- near-zero indexing time (no training step)
index = TurboQuantIndex(dim=128, bits=4, metric="ip")
database = torch.randn(100_000, 128)
index.add(database)

print(f"Vectors: {index.ntotal}")
print(f"Memory:  {index.memory_usage_mb:.1f} MB")
print(f"Index time: {index.last_add_time_ms:.0f} ms")

# Search
queries = torch.randn(10, 128)
distances, indices = index.search(queries, k=10)

# Save / load
index.save("my_index.pt")
loaded = TurboQuantIndex.load("my_index.pt")

LangChain RAG Pipeline

from langchain_huggingface import HuggingFaceEmbeddings
from pyturboquant.search.langchain import TurboQuantVectorStore

embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)

store = TurboQuantVectorStore.from_texts(
    texts=["Document 1...", "Document 2...", "Document 3..."],
    embedding=embeddings,
    bits=4,
)

docs = store.similarity_search("my query", k=3)
for doc in docs:
    print(doc.page_content)

# Use as a standard LangChain retriever
retriever = store.as_retriever(search_kwargs={"k": 5})

Class API (Stateful Quantizers)

from pyturboquant.core import MSEQuantizer, InnerProductQuantizer

# Reusable quantizer -- rotation matrix and codebook created once
mse_q = MSEQuantizer(dim=256, bits=3, seed=42)
ip_q = InnerProductQuantizer(dim=256, bits=4, seed=42)

for batch in dataloader:
    qt = mse_q.quantize(batch)
    reconstructed = mse_q.dequantize(qt)

Architecture

pyturboquant
├── core/              Pure PyTorch math (depends only on torch)
│   ├── rotation       Random orthogonal matrices via QR decomposition
│   ├── codebook       Lloyd-Max codebooks for Gaussian coordinates
│   ├── mse_quantizer  Algorithm 1: MSE-optimal vector quantization
│   ├── qjl            1-bit Quantized Johnson-Lindenstrauss transform
│   ├── prod_quantizer Algorithm 2: MSE + QJL for unbiased inner products
│   ├── packed         Bit-packing utilities for compact storage
│   └── types          QuantizedMSE, QuantizedIP, Codebook dataclasses
├── search/            Nearest neighbor search engine
│   ├── index          TurboQuantIndex with FAISS-like API
│   ├── distance       Asymmetric IP and L2 computation
│   └── langchain      LangChain VectorStore wrapper (optional)
└── utils/
    ├── beta_distribution  Sphere coordinate PDF (Lemma 1)
    └── metrics            MSE distortion, IP error, Shannon bounds

The core package has no dependencies beyond PyTorch. The search package builds on core. LangChain integration is guarded behind the [langchain] extra.

How It Works

TurboQuant exploits a key insight: after applying a random orthogonal rotation to a unit vector in high dimensions, each coordinate becomes approximately independent and Gaussian with variance 1/d. This allows optimal per-coordinate scalar quantization using precomputed Lloyd-Max codebooks.

MSE Quantizer (Algorithm 1):

1. Extract and store the vector norm
2. Normalize to the unit sphere
3. Apply a seeded random rotation
4. Quantize each coordinate independently via searchsorted against Lloyd-Max boundaries
5. Pack indices into compact bit representation

Inner Product Quantizer (Algorithm 2):

1. Apply the MSE quantizer at (bits - 1) bits
2. Compute the quantization residual
3. Apply a 1-bit QJL transform (random projection + sign) to the residual
4. At query time, estimate <x, y> ≈ <x_hat_mse, y> + QJL_estimate(<residual, y>)

The result is an unbiased inner product estimator that enables high-recall approximate nearest neighbor search with zero indexing time.

Empirical Distortion

Measured on 5,000 random unit vectors at d=256:

Bits	Empirical MSE	Shannon Lower Bound	Ratio
1	0.362	0.250	1.45x
2	0.117	0.0625	1.87x
3	0.034	0.0156	2.19x
4	0.009	0.00391	2.41x

These match the paper's Theorem 1 values within 1%.

Development

git clone https://github.com/jorgbahlmann/pyturboquant.git
cd pyturboquant
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Run tests (133 tests)
pytest

# Lint
ruff check src/ tests/

# Precompute codebooks (requires scipy)
python scripts/precompute_codebooks.py

# Run benchmarks
python benchmarks/bench_distortion.py
python benchmarks/bench_nn_search.py

Roadmap

• v0.1.0 (current) -- Core quantizers, NN search index, LangChain VectorStore
• v0.2.0 -- LlamaIndex VectorStore integration
• v0.3.0 -- Triton fused kernels for GPU hot paths
• v0.4.0 -- Haystack DocumentStore + Chroma / Weaviate adapters
• v0.5.0 -- IVF partitioning for sublinear search on billion-scale corpora
• Future -- KV cache compression backend for inference runtimes (vLLM / SGLang plugin)

Citation

@article{zandieh2025turboquant,
  title={TurboQuant: Online Vector Quantization with Near-Optimal Distortion Rate},
  author={Zandieh, Amir and Daliri, Majid and Hadian, Majid and Mirrokni, Vahab},
  journal={arXiv preprint arXiv:2504.19874},
  year={2025}
}

License

MIT