turbo-attn 0.6.2

Optimized CUDAgraph-enabled kernels and attention backend for vLLM, SGLang and more based on TurboQuant near-lossless KV cache compression. SOTA performance with Gemma 4, Qwen 3.6 and other modern LLMs.

Turbo Attention

A modular attention backend for vLLM, SGLang, and HuggingFace Transformers. Custom CUDA + Triton kernels with full CUDAGraph capture, asymmetric K/V quantization, hybrid-model support. Built on FlashAttention; based on TurboQuant near-lossless KV cache compression.

PyPI: turbo-attn · Import: tqkv · License: MPL-2.0

Install

pip install turbo-attn                  # codec + CUDA/Triton kernels
pip install "turbo-attn[vllm]"          # + vLLM attention backend
pip install "turbo-attn[all]"           # + SGLang, FlashInfer, flash-attn, eval harness

Quickstart

import torch
from tqkv import TurboKVCodec

codec = TurboKVCodec(head_dim=128, bit_width=4, device="cuda")
keys = torch.randn(8, 128, device="cuda")

packed, norms = codec.compress_k(keys)
recon = codec.decompress_k(packed, norms)

See examples/ for runnable snippets and ARCHITECTURE.md for a codebase tour.

Two independently-usable pieces

turbo-attn ships two pieces that are sold as a stack but designed to be consumed separately:

1. Codec → any attention backend. TurboKVCodec is a pure, framework-agnostic compressor: compress with TQKV, decompress to bf16 / fp16, hand the result to vanilla flash_attn_varlen_func, FlashInfer, SGLang attention, anything that takes raw KV. See examples/06_tqkv_codec_with_third_party_attention.py.

2. Kernels → any KV format. The cute-DSL prefill and split-K paged decode kernels are policy-parametric on the K/V format via the Loader extension point. The bundled set is {TqkvLoader, BypassLoader}:

   • TqkvLoader — TQKV centroid-based codec dequant (the production path).
   • BypassLoader — raw bf16 / fp16 KV, no codec. Useful for apples-to-apples ablations under an otherwise-byte-identical kernel. Third-party formats (fp8, int8, nvfp4, …) are not shipped — write a sibling Loader for your format. The Loader is the public extension surface; mainloop / scheduler / softmax / epilogue stay turbo-attn's. See docs/writing_a_loader.md for a worked fp8 example.

Repo layout

• tqkv/ — the package (codec, kernels, runtime, vLLM/SGLang plugins, calibration pipeline).
• tqkv/kernels/loaders/ — bundled cute-DSL prefill Loaders (tqkv, bypass).
• tqkv/kernels/_decode_loader_*.cuh — bundled decode Loaders (TqkvDecodeLoader, BypassDecodeLoader).
• docs/, docker/, scripts/, examples/, experiments/ — public docs, deploy recipes, helper scripts, runnable examples, research notes.
• The top-level internal/ directory is engineering-only and unsupported — design notes, internal compose files, dev harnesses. The wheel never ships it.

Run with Docker

Three inference servers are supported: vLLM, SGLang, and arbi-serve. Each ships a turn-key Dockerfile. Calibration files for the bit-width / model combo go in a host directory; the TQKV_CALIBRATION_FILE env var inside the container points to one. Examples below use Qwen3.5-0.8B + a K4V4 calibration.

Layout assumed

/path/to/models/Qwen3.5-0.8B/...                       # HF snapshot
/path/to/calibrations/qwen3.5-0.8b_tq4_v3.json         # calibration bundle

All three accept the same CLI flags: --kv-cache-dtype tqkv --attention-backend turbo-attn plus TQKV_BITS=<float> and TQKV_CALIBRATION_FILE=<path>. TQKV_BITS is the average bits-per-element across K and V (e.g. 4.0, 5.0, 6.0); a per-layer Lagrangian solver turns that target into a per-layer (k_bits, v_bits) allocation that lives in the calibration bundle. TQKV_BITS=4.0 does not mean "K and V both at 4 bits" — it means "average 4 bits-per-element under the smart per-layer allocation".

Sibling-checkout layout

All three Dockerfiles COPY from a sibling-repo layout. Clone the relevant repos as siblings of turbo-attn/:

GIT/
├── turbo-attn/        # this repo
├── vllm-fork/         # arbi-dev/vllm        (only needed for vLLM image)
├── sglang-fork/       # arbi-dev/sglang      (only needed for SGLang image)
└── arbi-serve/        # arbi-dev/arbi-serve  (only needed for arbi-serve image)

mkdir -p ~/GIT && cd ~/GIT
git clone https://github.com/arbi-dev/turbo-attn
git clone https://github.com/arbi-dev/vllm       vllm-fork    # for vLLM
git clone https://github.com/arbi-dev/sglang     sglang-fork  # for SGLang
git clone https://github.com/arbi-dev/arbi-serve              # for arbi-serve

vLLM

Uses our vllm-fork rebased onto upstream v0.20.1 (small overlay — CacheDType Literal relaxation, per-group block-pool bookkeeping, named TURBO_ATTN slot in AttentionBackendEnum; full layout in docker/PATCHES.md).

cd ~/GIT/turbo-attn/docker

# build + run
TQKV_MODELS_ROOT=/path/to/models \
  docker compose -f compose.vllm.yaml up -d --build

docker compose -f compose.vllm.yaml logs -f

# serve a request once "Application startup complete" appears:
curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "/models/Qwen3.5-0.8B", "prompt": "The capital of France is", "max_tokens": 12, "temperature": 0}'

Optional env (override on the docker compose command line):

Variable	Default	Purpose
TQKV_MODEL	/models/Qwen3.5-0.8B	Container-side model path
TQKV_BITS	4.0	Average bits-per-element target
TQKV_CALIBRATION_FILE	unset	Path to a v4 calibration bundle. Unset → uniform K4V4 fallback (tests/CI only; production needs a bundle)
TQKV_PORT	8000	Host port
TQKV_GPU_DEVICE	0	NVIDIA_VISIBLE_DEVICES
TQKV_MAX_MODEL_LEN	2048	Max context length

SGLang

Uses our sglang-fork rebased onto upstream v0.5.11 (small overlay — plugin registries for KV-cache dtypes and attention backends; full layout in docker/PATCHES.md).

cd ~/GIT/turbo-attn/docker

TQKV_MODELS_ROOT=/path/to/models \
  docker compose -f compose.sglang.yaml up -d --build

docker compose -f compose.sglang.yaml logs -f

curl http://localhost:30000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "/models/Qwen3.5-0.8B", "prompt": "The capital of France is", "max_tokens": 12, "temperature": 0}'

Same env-var contract as vLLM (drop TQKV_MAX_MODEL_LEN; SGLang uses TQKV_CONTEXT_LEN and TQKV_MEM_FRAC for --mem-fraction-static, default 0.45).

arbi-serve

Standalone OpenAI-compatible server with TQKV backends as a first-class citizen. Lives in arbi-dev/arbi-serve.

cd ~/GIT/arbi-serve

ARBI_MODELS_ROOT=/path/to/models \
  docker compose up -d --build

docker compose logs -f

curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "/models/Qwen3.5-0.8B", "prompt": "The capital of France is", "max_tokens": 12, "temperature": 0}'

MLA models

For DeepSeek V2/V3/V4, additionally set -e TQKV_MLA_ENABLE=1 on whichever container.

Calibration

Pre-built calibration bundles for common models live in HuggingFace at arbi-dev/turbo-attn-calibrations. To roll your own:

python -m tqkv.calibration.calibrate_centroids \
    --model Qwen/Qwen3.5-0.8B \
    --output qwen3.5-0.8b_tq4_v3.json \
    --bits 4

How it works

1. Rotate each KV vector with a fast Walsh–Hadamard transform.
2. Normalize — store the magnitude as a single BF16 value.
3. Quantize each rotated coordinate to a shared codebook.

Attention scores on rotated KV are bit-identical to attention on unrotated KV when the query is rotated by the same matrix; we pre-rotate Q once per request and compute everything in the rotated space.

The decode path is one fused CUDA kernel that unpacks, dequantizes, and runs Q·K, online softmax, and P·V in a single pass — no decompress buffer. Prefill has three paths: an FA4 CuTeDSL subclass that dequantizes inline during the MMA pipeline (default), a hand-written CUDA C++ kernel, and a decompress + stock FlashAttention fallback.

Configuration

All runtime configuration is via TQKV_-prefixed environment variables. The supported surface is below; anything unlisted is internal and may change.

Bit width and calibration

Variable	Default	Description
TQKV_BITS	4.0	Average bits-per-element target across K and V (float in [2.0, 8.0]). The runtime looks up the calibration bundle's byte_budget_table[<TQKV_BITS>] for the per-layer (k_bits, v_bits) allocation. Hard error if the entry is missing — no silent fallback.
TQKV_CALIBRATION_FILE	""	Path to a schema-v4 calibration bundle (centroids + per-channel scales + byte_budget_table). Required for production; bundle generation: calibrate_centroids → optimize_quant → migrate_v3_to_v4. When unset, the plugin falls back to uniform K4V4 (tests/CI only).
TQKV_AUTO_CALIBRATE_MODEL	""	Model path for plugin-side auto-calibration when TQKV_CALIBRATION_FILE doesn't exist on first init.

Engine selection

Variable	Default	Description
TQKV_ENGINE	"" (auto)	Decode engine: native_tq, flash_attn, or bypass.
TQKV_PREFILL_ENGINE	fa4	Prefill path: fa4, adaptive, or decomp_fa_main_only (bench-only — main-token prefill through decomp+FA; decode + MTP verify untouched).
TQKV_PREFILL_BYPASS	1	First-chunk prefill bypass — skip codec on prompt-prefill, then re-rotate to TQ basis for decode.
TQKV_FUSE_QROT	"" (auto)	Fused Q-rotation prologue. Decode-only.
TQKV_O_PROJ_FOLD	on	Fold rotate_output into o_proj weights.
TQKV_MTP_SPLITK	1	Use split-K decode kernel for MTP layers.
TQKV_DECODE_SPLITS	"" (autotune)	Force decode-kernel split count.

Backend behaviour

Variable	Default	Description
TQKV_NO_JIT	0	Fail if a kernel variant is not pre-compiled.
TQKV_K_NC	1	Apply norm-correction to K reads in the dequant path.
TQKV_DISABLE_PRESCALE	0	Disable per-channel pre-scaling on compress upload.
TQKV_STRICT_NO_SDPA	0	Raise instead of taking the head_dim>256 SDPA fallback. Recommended for head_dim>256 deployments.

MLA (DeepSeek V2/V3/V4)

Variable	Default	Description
TQKV_MLA_ENABLE	0	Master switch for the MLA backend.
TQKV_MLA_ROPE_HEAD_DIM	64	RoPE head dimension for MLA latent + RoPE split.

Why a vLLM fork (for now)

CacheDType in vllm/config/cache.py is a Pydantic Literal validated at class-definition time, which blocks runtime registration of new KV-cache dtypes. Until that's relaxed upstream, we ship a fork. The fork is a thin overlay; full layout in docker/PATCHES.md. SGLang does not need a fork.

Citation

If Turbo Attention helps your work, please cite both the underlying TurboQuant paper and this implementation:

@misc{turbo_attention2026,
  title = {Turbo Attention: Production attention backend for TurboQuant KV cache compression},
  author = {Evseev, Dmitri},
  year = {2026},
  url = {https://github.com/arbi-dev/turbo-attn}
}

@inproceedings{zandieh2026turboquant,
  title = {TurboQuant: Near-optimal KV Cache Quantization for LLM Inference},
  author = {Zandieh, Amir and others},
  booktitle = {ICLR},
  year = {2026}
}

License

Mozilla Public License 2.0 (MPL-2.0). See LICENSE and NOTICE.