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VC-Attention is proposed, a training-free low-bit attention framework that addresses diffusion Transformers and quantization scale by pairing Value smoothing with a fused probability Cast, and improves fidelity over low-bit baselines.
FreeToken transforms personal machines into powerful platforms for running massive AI models, enabling users to deploy frontier-scale intelligence without specialized infrastructure.
K-means, previously relegated to offline processing, gets a 17.9x speed boost on modern GPUs thanks to Flash-KMeans' clever IO and contention optimizations.
Get 2x faster video generation from diffusion transformers without sacrificing quality, thanks to a clever parameter-free error compensation technique.
SpargeAttention2 achieves 95% attention sparsity in video diffusion models with a 16.2x speedup, proving that trainable sparse attention can significantly outperform training-free methods without sacrificing generation quality.
Forget sparse KV caches – QuantSpec's hierarchical 4-bit quantization unlocks 2.5x speedups in long-context LLM inference with >90% acceptance rates.
