TileMix Introduces Tile-Centric Precision-Routing Kernel for LLM Inference Acceleration
A recent paper on arXiv (identifier 2608.17336) introduces TileMix, a precision-routing kernel designed to enhance the inference of large language models (LLMs). This innovative approach tackles the challenges of computation and memory traffic during long-context prefill, where dense self-attention calculates quadratic query-key scores. Unlike traditional methods, TileMix employs spatial precision routing across hardware-aligned score tiles. It divides the attention matrix into score tiles, encodes routing decisions as bitmasks, and utilizes FP16 or INT8 for score computations while maintaining a shared online-softmax state. The scalable precision grouping enables each routing bit to influence multiple adjacent key tiles, ultimately aiming to lower the quadratic costs associated with long-context processing and boost prefill speed and memory bandwidth in LLM inference.
Key facts
- TileMix is a tile-centric precision-routing kernel for LLM inference acceleration.
- The paper is available on arXiv with identifier 2608.17336 and announcement type 'New'.
- Long-context prefill in LLMs incurs substantial computation and memory traffic due to quadratic query-key scores.
- Existing methods use uniform low-precision paths or select token interactions, leaving spatial precision routing outside fused dense attention.
- TileMix makes numerical precision an executable spatial decision over score-tile groups.
- The kernel partitions the attention matrix into hardware-aligned score tiles and packs routing decisions into compact bitmasks.
- Tile groups are dispatched through FP16 or INT8 score computation, with both paths updating a shared online-softmax state.
- Scalable precision grouping allows each routing bit to govern multiple adjacent key tiles, preserving hardware-aligned compute tiles and compact metadata at long contexts.
Entities
Institutions
- arXiv