Making LLMs Faster & Smarter with Sparse Attention
A novel approach to reduce computational complexity in large language models
SeerAttention introduces a new mechanism that dynamically identifies which attention connections matter most, significantly reducing computational demands without sacrificing performance.
- Addresses the quadratic complexity bottleneck in attention mechanisms
- Creates intrinsic sparsity that adapts to different contexts
- Improves both efficiency and scalability for long-context processing
- Offers a simpler alternative to predetermined sparse patterns
This engineering breakthrough has significant implications for deploying more efficient LLMs in resource-constrained environments and processing longer contexts with existing hardware.
SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs