Nunchux AI 推出 VC-Attention：免訓練低位注意力核心，加速影片擴散 Transformer

Nunchux AI has released VC-Attention, a training-free low-bit attention kernel built for video Diffusion Transformers (DiTs).It targets 2 problems at once: value quantization error and a slow softmax stage.

Why Attention is the Video Bottleneck Video DiTs flatten a clip into 1 sequence of spatiotemporal tokens and run full self-attention at every layer.A 5-second 720p Wan2.2-14B clip spans about 70K tokens.On the RTX 5090, attention takes more than 64% of generation time.

The research team states that attention is about two thirds of every MiniMax-H3 denoising step on a single B200.Low-bit Tensor Cores speed up the 2 matrix products, QK and PV.2 obstacles remain.First, prior methods like SageAttention2 smooth queries and keys.

After QK smoothing and rotation, the value term accounts for 82% of output error on Wan2.2.Second, the softmax between the products still runs in FP32.On B200 and H200, that exponential and its FP8 cast become the longest pipeline stage.

V-Smooth: Fixing Value Outliers Value outliers sit in a few tokens, and their channels shift across heads, layers, and steps.A Hadamard rotation preserves token norms, so it does not remove them.Rotating V changes value error by just 0.2%.

V-Smooth takes a different route: Group: An online k-means clusters value tokens per batch and head.Keys and values are permuted together, so non-causal attention output is unchanged.Demean: Each 128-token hardware block subtracts its mean.

Only the residual is quantized, using per-channel E4M3 at 8 bits or NVFP4 at 4 bits.Restore: The mean is added back using the row sum online softmax already keeps.No second pass or extra buffer is needed.Averaged over 100 Wan2.2 heads, the block mean removes 8% of block energy in sequence order.

It removes 12% under DeltaQuant’s static cube and 36% after sorting.Each mean costs 0.125 bit per value element.Grouping runs only on the first 25% of denoising steps.The permutation is reused across 4 adjacent steps.Averaged over the full schedule, grouping costs 3 to 4% of attention time.

ExpCast-FP8: Removing the Softmax Bottleneck An E4M3 byte is already close to a logarithm of the value it stores.Read as an integer, it equals roughly 8 log2(v) + 56.So ExpCast-FP8 writes the byte directly from the log-domain score with 1 fused multiply-add.The constant β = -0.

35 centers the leftover error, and no constant is fitted per model.The direct path writes the same byte as the FP32 exponent-then-cast path on 79.6% of each doubling.Elsewhere it lands 1 code away.The paper proves a per-row total variation bound under 3.64%, plus any underflow tail.Across 204.

8K Wan2.2 attention rows, the measured average is 1.6%.ExpCast-FP8 applies only to the 8-bit kernel, since NVFP4 has no single affine log-to-code map.Hand-written CuTe/CUDA fusion of the preprocessing chain cuts 1 V-Smooth call from 42.2 ms to 4.8 ms on B200.

Explainer: How VC-Attention Works #mtp-vcattn-embed{background:#16142A!important;border:1px solid #39345E!important;border-radius:14px!important;overflow:hidden!important;margin:24px 0!important;padding:0!important;color:#EDEBFA!important;max-width:100%!

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important} @media (max-width:640px){#mtp-vcattn-embed{border-radius:10px!important;margin:18px 0!important}} (function(){ var f=document.getElementById('mtp-vcattn-frame'); window.addEventListener('message',function(e){ if(!f||e.source!==f.contentWindow||!e.data||e.data.type!

=='mtp-vcattn-h')return; var h=parseInt(e.data.h,10); if(h>200&&h<6000)f.style.height=h+'px'; }); })(); Benchmarks Tests cover 4 open-weight video DiTs: Wan2.2-T2V-A14B, LongCat-Video, HunyuanVideo-1.5, and MiniMax-H3.Fidelity is scored against BF16 FlashAttention-4 outputs over 100 prompts.

GPU (Wan2.2)PrecisionAttention speedupEnd-to-end speedupB2008-bit1.59×1.19×H2008-bit1.46×1.13×RTX PRO 60004-bit2.27×1.36×RTX 50904-bit3.58×1.70× On B200, VC-Attention is 6.02× faster than SageAttention2, which ships no Blackwell kernel.On H200, the gap is 1.16×.

On workstation cards, 4-bit V-Smooth matches SageAttention3 on the RTX PRO 6000.It stays within 5% on the RTX 5090, so fidelity separates them.Fidelity results: At 8 bits, V-Smooth adds 2.3 dB PSNR over SageAttention2 on Wan2.2 and 2.8 dB on HunyuanVideo-1.5.Adding ExpCast-FP8 gives back 0.7 to 2.

1 dB but still beats SageAttention2 on all 4 models.At 4 bits, V-Smooth beats SageAttention3 by 2.9 dB on Wan2.2 and 3.6 dB on LongCat-Video.Run training-free, Attn-QAT falls 3.4 to 6.7 dB below SageAttention2.On MiniMax-H3 at 1344×768, attention runs 1.60× faster than BF16 FlashAttention-4 on B200.

PSNR is 20.2 dB versus 19.9 dB for SageAttention2.On B300, the paper reports 1.47× versus 1.31× for a naive FP8 kernel.The blog chart lists 1.51× for B300.Nunchux Attention, the company's proprietary extension, reaches 1.91× on B200 and 1.83× on B300 for MiniMax-H3 attention.

The method changes only per-interaction cost.So it can compose with sparse attention like Sparse VideoGen and Radial Attention, and distillation and multi-GPU execution.Nunchux says free MiniMax-H3 access is coming through its Modelverse waitlist.

Key Takeaways VC-Attention is training-free low-bit attention for video DiTs from Nunchux AI.V-Smooth clusters value tokens, then quantizes only residuals after block-mean subtraction.ExpCast-FP8 replaces the FP32 exponential and cast with 1 multiply-add.Wan2.2 attention runs 1.

59× faster on B200 and 3.58× on RTX 5090.No public kernel release yet; Nunchux runs a proprietary extension in its stack.Check out the Paper and Technical details.All credit goes to the researcher of this project.

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