Tiled Flash Linear Attention (TFLA) introduces optimized kernels for linear RNNs that achieve theoretical computational advantages in practice.

• Implements chunkwise-parallel processing for more efficient memory usage
• Demonstrates competitive performance compared to Transformers in language modeling
• Achieves linear compute scaling with sequence length instead of quadratic
• Provides practical runtime advantages through custom kernel optimization

This engineering breakthrough matters because it enables more efficient sequence processing for long contexts, potentially reducing computational costs while maintaining model quality.

Tiled Flash Linear Attention: More Efficient Linear RNN and xLSTM Kernels