turboquant-torch

Unofficial PyTorch reference implementation of TurboQuant from Google Research (ICLR 2026).

Paper: TurboQuant: Redefining AI Efficiency with Extreme Compression Blog: Google Research Blog

TurboQuant is a two-stage online (data-oblivious) vector quantizer that achieves near information-theoretic optimal distortion. No training data needed — just plug in and compress.

Overview

How It Works

flowchart TD
    X["Input x"] --> Norm["Store ‖x‖"]

    subgraph S1["Stage 1: MSE-Optimal Quantizer (b−1 bits)"]
        Norm --> Normalize["Normalize to unit vector"]
        Normalize --> RHT["Randomized Hadamard Transform\n(random sign flip + FWHT)"]
        RHT --> LM["Lloyd-Max Scalar Quantizer"]
        LM --> Codes["codes (b−1 bits/coord) + norm (32 bits)"]
    end

    Codes --> Deq["x̂ = dequantize(codes, norm)"]
    X --> Res
    Deq --> Res["Residual r = x − x̂"]

    subgraph S2["Stage 2: QJL 1-bit on Residual"]
        Res --> Proj["Random Gaussian Projection"]
        Proj --> Sign["sign()"]
        Sign --> Bits["sign bits (1 bit/coord)"]
    end

    Codes --> Out["Total: b bits per coordinate\n(unbiased inner product estimator)"]
    Bits --> Out

Key Properties

• Online / data-oblivious — no training, no calibration data, no k-means
• Near-optimal — within ~2.7x of Shannon lower bound
• Accelerator-friendly — all ops are vectorizable (no branching)
• Zero indexing time — vs Product Quantization which needs k-means training

Installation

pip install turboquant-torch

From source (development)

git clone https://github.com/codepawl/turboquant-torch.git
cd turboquant-torch
pip install -e ".[dev]"

3 Lines to Compress Any LLM

import turboquant
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")

model = turboquant.wrap(model)  # <-- one line
output = model.generate(**tokenizer("Hello", return_tensors="pt"), max_new_tokens=50)

KV cache automatically compressed to 3-bit. ~10x less memory.

For more control:

model = turboquant.wrap(
    model,
    bit_width=3,              # 2, 3, or 4
    residual_length=128,      # sliding window
    n_outlier_channels=8,     # outlier routing
    verbose=True,             # print stats
)

Or use the cache directly:

from turboquant import TurboQuantDynamicCache

cache = TurboQuantDynamicCache.from_model(model)
output = model.generate(**inputs, past_key_values=cache, max_new_tokens=50)

Low-Level API

Basic Quantize / Dequantize

import torch
from turboquant import TurboQuant

tq = TurboQuant(dim=128, bit_width=3, unbiased=True)

x = torch.randn(100, 128)
output = tq.quantize(x)
x_hat = tq.dequantize(output)

print(f"Compression: {tq.compression_ratio():.1f}x")  # ~10.7x

KV Cache Compression

from turboquant import TurboQuantKVCache

cache = TurboQuantKVCache(head_dim=128, bit_width=3, residual_length=128)

# Compress KV tensors (batch, heads, seq, dim)
keys = torch.randn(2, 32, 2048, 128)
values = torch.randn(2, 32, 2048, 128)
compressed = cache.compress(keys, values)

# Attention with compressed cache
query = torch.randn(2, 32, 1, 128)
output = cache.attention(query, compressed)

orig_mb, comp_mb, ratio = cache.memory_savings(2, 32, 2048)
print(f"Memory: {orig_mb:.0f} MB -> {comp_mb:.0f} MB ({ratio:.1f}x)")

Real Model Example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from turboquant import TurboQuantKVCache

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", dtype=torch.float32)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")

with torch.no_grad():
    out = model(**tokenizer("The quick brown fox", return_tensors="pt"), use_cache=True)

kv = out.past_key_values
k, v = kv.layers[0].keys, kv.layers[0].values  # layer 0 KV cache
cache = TurboQuantKVCache(head_dim=k.shape[-1], bit_width=3)
compressed = cache.compress(k.float(), v.float())

Sliding Window (Residual Buffer)

Keep recent tokens in fp16 for higher accuracy on local context:

cache = TurboQuantKVCache(
    head_dim=128,
    bit_width=3,
    residual_length=128,  # last 128 tokens stay in fp16
)

GQA/MQA Support

For models with grouped query attention (Llama-3, Mistral, etc.):

cache = TurboQuantKVCache.for_gqa(
    head_dim=128,
    num_kv_heads=8,       # Llama-3-8B
    num_query_heads=32,
    bit_width=3,
    residual_length=128,
)
# Keys auto-bumped to 4-bit to compensate for GQA error amplification

Outlier Channel Routing

Preserve high-magnitude channels in full precision (inspired by KVQuant/GEAR):

cache = TurboQuantKVCache(
    head_dim=128,
    bit_width=3,
    n_outlier_channels=8,   # top-8 channels kept in fp16
    residual_length=128,
)

Adaptive Per-Layer Bit Allocation

Different layers get different bit budgets:

from turboquant import AdaptiveKVCache, gradient_allocation

# Manual: 2-bit for early layers, 4-bit for late layers
cache = AdaptiveKVCache(
    head_dim=128,
    layer_bits=[2]*10 + [3]*12 + [4]*10,  # 32 layers total
)

# Gradient allocation: smooth 2→4 bit ramp
bits = gradient_allocation(n_layers=32, min_bits=2, max_bits=4, strategy="linear")
cache = AdaptiveKVCache(head_dim=128, layer_bits=bits)

# Auto-calibrated from model (requires HuggingFace model)
cache = AdaptiveKVCache.from_model(
    model, tokenizer,
    head_dim=128,
    target_avg_bits=3.0,
)
print(cache.summary())

Vector Search

from turboquant import TurboQuantIndex

index = TurboQuantIndex(dim=128, bit_width=3, metric="ip")
index.add(database_vectors)  # Near-instant, no training!
scores, indices = index.search(query, k=10)

Real-World Usage

Compress a live model's KV cache

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from turboquant import TurboQuantKVCache

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", dtype=torch.float32)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")

inputs = tokenizer("Explain quantum computing in simple terms:", return_tensors="pt").to(model.device)
with torch.no_grad():
    out = model(**inputs, use_cache=True)

past_kv = out.past_key_values
head_dim = past_kv.layers[0].keys.shape[-1]
cache = TurboQuantKVCache(head_dim=head_dim, bit_width=3, residual_length=0)

for i in range(len(past_kv.layers)):
    k, v = past_kv.layers[i].keys.float(), past_kv.layers[i].values.float()
    compressed = cache.compress(k, v)
    k_hat = cache.decompress_keys(compressed)
    print(f"Layer {i}: MSE={((k - k_hat)**2).mean():.6f}")

Generate text with compressed KV cache

from transformers import DynamicCache

with torch.no_grad():
    out = model(**inputs, use_cache=True)

new_cache = DynamicCache()
for i in range(len(out.past_key_values.layers)):
    k = out.past_key_values.layers[i].keys.float()
    v = out.past_key_values.layers[i].values.float()
    compressed = cache.compress(k, v)
    k_hat = cache.decompress_keys(compressed).to(k.dtype)
    v_hat = cache.decompress_values(compressed).to(v.dtype)
    new_cache.update(k_hat, v_hat, i)

outputs = model.generate(
    **inputs, past_key_values=new_cache,
    max_new_tokens=50, do_sample=False,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Multi-layer adaptive compression

from turboquant import AdaptiveKVCache

adaptive = AdaptiveKVCache.from_model(
    model, tokenizer, head_dim=head_dim, target_avg_bits=3.0
)
print(adaptive.summary())

for i in range(adaptive.n_layers):
    k = past_kv.layers[i].keys.float()
    v = past_kv.layers[i].values.float()
    compressed = adaptive.compress_layer(i, k, v)

Compatibility

Works with any standard transformer KV cache:

Model Family	Status	Notes
Llama-3 / 3.1 / 3.2	Full support	GQA-aware mode recommended
Mistral / Mixtral	Full support	Sliding window auto-detected
Gemma / Gemma 2	Full support
Qwen2.5 / Qwen3	Full support
Phi-3 / Phi-4	Full support
Command-R	Full support
DeepSeek-V2/V3	Skip MLA layers	KV already compressed by MLA
Qwen3.5 / Jamba	Attention layers only	Non-attention layers skipped
T5 / BART / mBART	Partial	Self-attention KV only
Mamba / RWKV	Not applicable	No KV cache (SSM/RNN)

Use compress_model_kv() for automatic handling:

from turboquant.compat import compress_model_kv

compressed_cache = compress_model_kv(past_key_values, model, bit_width=3)
outputs = model.generate(**inputs, past_key_values=compressed_cache, max_new_tokens=50)

Distortion vs Bit Width

From paper Table 1 (MSE distortion on unit vectors):

Bits/coord	MSE Distortion	Compression Ratio
1	~0.36	32x
2	~0.117	16x
3	~0.03	10.7x
4	~0.009	8x

3-bit achieves zero quality loss on LongBench, Needle-in-Haystack, ZeroSCROLLS, RULER, and L-Eval benchmarks.

KV Cache Memory Savings

Benchmarks on Real Models

Tested on SmolLM2-135M KV cache (30 layers, 3 KV heads, head_dim=64):

Bit-width	Key MSE	Attn Score MSE	Memory	Ratio
2-bit	1.8732	0.01798362	0.03 MB	12.8x
3-bit	0.5902	0.00741907	0.04 MB	9.1x
4-bit	0.1740	0.00249073	0.06 MB	7.1x

Full benchmark results: benchmarks/results.md

KV Cache Memory at Scale

Downstream Task Evaluation

Tested on Qwen3.5-4B (head_dim=256, 3-bit, RTX 3060, 200 samples/task):

Task	fp16	3-bit	Diff
HellaSwag	37.0%	38.5%	+1.5%
ARC-Easy	49.0%	49.5%	+0.5%

Differences are within sampling variance, confirming compression preserves task accuracy.

Sliding Window Effect

GQA Error Amplification

Project Structure

turboquant/
├── __init__.py          # Public API
├── hadamard.py          # Fast Walsh-Hadamard Transform + random rotation
├── codebook.py          # Lloyd-Max optimal scalar quantizer codebooks
├── qjl.py               # Quantized Johnson-Lindenstrauss (1-bit)
├── mse_quantizer.py     # MSE-optimal quantizer (rotation + Lloyd-Max)
├── core.py              # TurboQuant two-stage pipeline
├── kv_cache.py          # KV cache compression for transformers
├── outlier.py           # Outlier channel detection and routing
├── adaptive.py          # Adaptive per-layer bit allocation
├── compat.py            # Model architecture compatibility detection
└── vector_search.py     # Approximate nearest neighbor index

Differences from Paper

Aspect	Paper	This Repo
Framework	JAX/XLA	PyTorch
CUDA kernels	Custom fused kernels for H100	Pure PyTorch (no custom CUDA)
Entropy coding	Optional (Section 3.1)	Not implemented
HuggingFace	N/A	KV cache compression (examples/)
Codebook	Exact precomputed	Lloyd-Max iterative (equivalent)

Custom CUDA kernels for fused Hadamard + quantize operations would be a valuable future contribution.

Examples

Notebook	What it shows
01_quickstart.ipynb	3-line compression, before/after comparison
02_long_context.ipynb	Memory scaling, sliding window, outlier routing
03_adaptive_compression.ipynb	Per-layer sensitivity, adaptive bit allocation

Each notebook has an "Open in Colab" badge and runs on CPU (free tier).

Running Tests

pip install -e ".[dev]"
pytest tests/unit/ -v              # fast, isolated
pytest tests/integration/ -v       # multi-module
pytest tests/unit/ tests/integration/ -v  # all

Contributing

We welcome contributions! Here's how to get started:

1. Fork the repo and create a feature branch from staging
2. Install dev dependencies: pip install -e ".[dev]"
3. Make changes and add tests
4. Run checks:

   ruff check turboquant/ tests/
   ruff format turboquant/ tests/
   mypy turboquant/
   pytest tests/ -v

5. Open a PR against staging (not main)

See our branching strategy: feature branches → staging → main.

Areas where help is needed

• CUDA/Triton kernels — fused Hadamard + quantize for 10x speedup
• vLLM integration — PagedAttention compatibility
• More model benchmarks — Llama-3, Mistral, Gemma on downstream tasks
• Entropy coding — optional compression from paper Section 3.1

Community

• GitHub Issues: github.com/codepawl/turboquant-torch/issues
• Discord: discord.gg/7fydHgK6kA
• X: @codepawl

Citation

@inproceedings{turboquant2026,
  title={TurboQuant: Redefining AI Efficiency with Extreme Compression},
  author={Zandieh, Amir and Daliri, Majid and Hadian, Majid and Mirrokni, Vahab},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2026},
  url={https://arxiv.org/abs/2504.19874}
}

Related Work

• QJL: 1-Bit Quantized JL Transform — the 1-bit quantizer used in Stage 2. The official QJL implementation by the paper authors is available at github.com/amirzandieh/QJL.
• PolarQuant — related polar coordinate quantization approach

Documentation

• Quick Start
• API Reference
• Model Compatibility
• Changelog

License

MIT