FlashAttention-4 Hits 1,600 TFLOPs/s on Blackwell — the Post-MatMul Era Accelerates

New kernel optimizations for NVIDIA's Blackwell GPUs push attention computation to unprecedented throughput, while CliffordNet shows that better math can replace brute-force parameter scaling.

Two separate developments this weekend point toward a maturing understanding that raw model size is no longer the primary lever for AI progress — kernel engineering and mathematical elegance are catching up. FlashAttention-4, the latest iteration of Tri Dao's attention optimization framework, has been benchmarked at approximately 1,600 TFLOPs/s on NVIDIA's Blackwell GPUs, according to @askalphaxiv. That's a staggering throughput figure that makes previously impractical context lengths and batch sizes viable for production workloads.

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