turboquant-vllm 1.3.0

TurboQuant KV cache compression for vLLM — fused Triton kernels, 3.76x compression, 3.7x faster decode on RTX 4090

turboquant-vllm

TurboQuant KV cache compression as a drop-in vLLM plugin. 3.76x compression, near-identical output quality, one CLI flag to enable.

First open-source TurboQuant implementation — paper to working vLLM plugin in 72 hours.

Install

pip install turboquant-vllm[vllm]

Or with uv:

uv add turboquant-vllm --extra vllm

Quick Start (vLLM)

The TQ4 attention backend registers automatically via vLLM's plugin system:

vllm serve allenai/Molmo2-4B --attention-backend CUSTOM

No code changes required. The plugin compresses KV cache pages to 68 bytes/token/head (vs 256 bytes FP16).

Quick Start (HuggingFace)

from transformers import DynamicCache
from turboquant_vllm import CompressedDynamicCache

cache = DynamicCache()
compressed = CompressedDynamicCache(cache, head_dim=128, bits=4)

# Pass cache (not the wrapper) to model.generate()
# Compression happens transparently on every cache.update()

Benchmark Results

Molmo2-4B (bfloat16, 36 layers) on RTX 4090 — 11K visual tokens from 2fps video + 256 generation tokens:

Mode	KV Cache	Compression	Output Quality	Overhead
FP16 baseline	1,639 MiB	1.0x	--	--
TQ3 (3-bit)	845 MiB	1.94x	~95% cosine similarity	2.35x
TQ4 (full dequant)	435 MiB	3.76x	~97% cosine similarity	3.36x
TQ4 (incremental)	435 MiB	3.76x	~97% cosine, 100+ matching tokens	1.78x

How It Works

Implements Google's TurboQuant algorithm (ICLR 2026):

1. Random orthogonal rotation maps each KV vector onto coordinates that follow a known Beta distribution
2. Lloyd-Max scalar quantization finds optimal centroids for that distribution at 3-4 bits per coordinate
3. Nibble packing stores two 4-bit indices per byte for 3.76x compression
4. Incremental dequantization only decompresses new tokens each decode step, keeping overhead at 1.78x

What Gets Compressed

Data	Compressed	Format
Key cache vectors	Yes	uint8 nibble-packed indices + fp32 norms
Value cache vectors	Yes	uint8 nibble-packed indices + fp32 norms
Rotation matrices	No	Generated once per layer from fixed seed
Lloyd-Max codebook	No	Computed once, shared across all layers

Roadmap

• Core TurboQuant algorithm (Lloyd-Max, MSE quantizer, compressors)
• CompressedDynamicCache with incremental dequantization
• vLLM TQ4 attention backend plugin
• Fused Triton kernels (17.8x Q@K^T speedup, Flash Attention fusion)
• Container image with turboquant-vllm baked in
• Full Flash Attention fusion with fp32 online softmax
• SageAttention-style INT8 path

Documentation

• Architecture -- Module map, dependency DAG, data flow diagrams
• Roadmap -- Detailed implementation status and experiment results
• Development Guide -- Setup, build, test, lint commands

Citation

@inproceedings{zandieh2025turboquant,
  title={TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate},
  author={Zandieh, Amir and Han, Insu and Daliri, Majid and Karbasi, Amin},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2025}
}

License

Apache 2.0