MiniMax 重点介绍了与 Nunchux AI 等合作者共同推出的 VC-Attention：一种无需重训练的低比特注意力方法，可在 B200 上较 FlashAttention-4 提速 1.6 倍、B300 上提速 1.5 倍，同时保真度优于 SageAttention2。
Three paths to faster video attention: compute the same interactions more efficiently, compute fewer in full, or change how information is mixed. Here’s a visual guide. 👇 Thanks to Nunchux AI and collaborators for VC-Attention, bringing training-free low-bit acceleration to MiniMax-H3, with better fidelity than SageAttention2 in the B200 evaluation. The approach balances speed and fidelity: V-Smooth reduces value quantization error, while ExpCast-FP8 makes softmax faster through approximation. Excited to see the community keep building on H3. Could combining low-bit computation with sparse methods like Sol-Attn push efficiency further? We’re looking forward to seeing that explored.
Introducing VC-Attention: fast and accurate low-bit attention without retraining. On MiniMax-H3, VC-Attention speeds up attention by 1.6× on B200 and 1.5× on B300 over FlashAttention-4, with better fidelity than SageAttention2. It also works with existing sparse attention methods. Two key innovations: • V-Smooth reduces value quantization error. • ExpCast-FP8 speeds up softmax. Nunchux Attention, our proprietary extension, pushes the speedup to 1.9× on B200 and 1.8× on B300. Blog: https://t.co/k0wDVujvBB Technical Report: https://t.co/wMWfga6m73 Joint work by researchers at MIT, CMU, UC Berkeley, Stanford, and NVIDIA.
