Nunchux AI Introduces VC-Attention: A Training-Free Low-Bit Attention Kernel That Speeds Up Video Diffusion Transformers
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
VC-Attention is a training-free method that speeds up the attention mechanism in video diffusion transformers, which are AI models used to generate videos. The technique addresses two performance bottlenecks: errors that occur when compressing numerical values to lower bit depths, and the computational cost of the softmax operation. The method uses value smoothing to reduce quantization errors and a specialized approach called ExpCast-FP8 to replace slow floating-point calculations with faster operations. Benchmarks show speedups ranging from 1.59x to 3.58x depending on the hardware used, with minimal loss in output quality.
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Anthropic releases new Mythos and Fable models amid RSI concerns
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Also reported by The Verge, SiliconANGLE and MarkTechPost
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Gemini 4 Carbon is now available
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Also reported by SiliconANGLE
Introducing AlphaProof Code: a breakthrough in verifying complex software at depth
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