sparse-attention
NVIDIA shipped a drop-in kernel that nearly halves video generation time News
NVIDIA released code on July 28 for Sol-Attn, an attention kernel that decides which parts of a long video to compute exactly while approximating the rest inside a single pass, reporting up to 2.1 times faster video generation with no retraining and no weight changes.
llama.cpp Merges MiniMax M3's Sparse Attention, Because Running It Dense Gives Wrong Answers News
Support for MiniMax M3's block-sparse attention landed in llama.cpp today, and the contributor is explicit that it is not a speed optimisation: the model was trained sparse, so running it dense produces degraded output.
A new attention method learns what to ignore, and reads 64x more text News
Tencent's Hunyuan team introduced HiLS, a sparse-attention method that learns end-to-end which parts of a long document to focus on, matching full attention while handling context 64 times longer than it was trained on.
Sparse Attention Lesson
Sparse attention lets a transformer skip most of the pairwise comparisons between tokens, so instead of every token attending to every other token, each one attends to a chosen subset -- which is what makes million-token context windows affordable.
Sol-Attn (Sol-Engine) Tool
NVIDIA's drop-in sparse attention kernel for long-video diffusion transformers, released July 28 for HunyuanVideo-13B and Wan2.1-T2V-14B. Screens compressed key/value blocks inside a single online-softmax pass, so exact attention goes where it matters and skipped blocks get an approximate correction. Training-free, no weight changes, reported up to 2.1x for generation and 2.3x for editing. The repo marks end-to-end re-benchmarks for the two integrated pipelines as pending.