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TileMix Accelerates LLM Inference with Mixed-Precision Attention.

Hanzhi Zhang, Qiao Zhang, Qinglei Cao, Heng Fan, Yan Huang, Kewei Sha, Yunhe Feng· August 19, 2026 View original

Key takeaways

  • TileMix optimizes LLM inference by applying mixed-precision to attention matrix tiles.
  • It dynamically routes computations through FP16 or INT8 paths.
  • The method improves throughput and recovers accuracy lost by uniform INT8.
  • TileMix supports various LLM features and requires no model retraining.

Who benefits

AI DevelopmentCloud ComputingData CentersTelecommunications

Summary

TileMix is a new kernel that optimizes large language model inference, especially for long contexts, by applying mixed-precision computation to attention matrix tiles. It dynamically routes parts of the attention calculation through FP16 or INT8 paths, improving throughput while maintaining accuracy.

Large language models (LLMs) face significant computational and memory challenges, particularly during long-context prefill, due to the quadratic complexity of dense self-attention. Existing optimization methods often use uniform low-precision or selective token interactions, but they don't fully leverage spatial precision routing within fused dense attention. This paper introduces TileMix, a novel tile-centric precision-routing kernel designed to accelerate LLM inference. TileMix partitions the attention matrix into hardware-aligned tiles and makes dynamic precision decisions (FP16 or INT8) for each tile group. These routing decisions are compactly encoded, allowing both precision paths to update a shared online-softmax state efficiently. TileMix preserves dense token connectivity, requires no retraining, and supports various LLM features like grouped-query attention and variable-length batches. Benchmarks on A100 GPUs with models like LLaMA and Qwen show that TileMix recovers quality lost by uniform INT8 precision and significantly boosts prefill throughput compared to FP16, offering a flexible balance between accuracy and efficiency.

Why it matters

For professionals working with large language models, especially those requiring long context windows, TileMix offers a way to significantly improve inference speed and reduce computational costs without sacrificing model quality. This can lead to more efficient deployment and lower operational expenses.

How to implement this in your domain

  1. 1Explore integrating the TileMix kernel into existing LLM inference pipelines, especially for models with long context requirements.
  2. 2Benchmark TileMix performance against current FP16 or uniform INT8 inference methods on specific LLM workloads.
  3. 3Evaluate the accuracy-efficiency trade-offs offered by TileMix's controllable precision routing for different applications.
  4. 4Consider contributing to or adopting the open-source implementation to leverage its benefits.

Original post by Hanzhi Zhang, Qiao Zhang, Qinglei Cao, Heng Fan, Yan Huang, Kewei Sha, Yunhe Feng

"arXiv:2608.17336v1 Announce Type: new Abstract: Long-context prefill in large language models (LLMs) incurs substantial computation and memory traffic because dense self-attention computes quadratic query-key scores. Existing methods either use a uniform low-precision path or sel…"

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Primary sources

Originally posted by Hanzhi Zhang, Qiao Zhang, Qinglei Cao, Heng Fan, Yan Huang, Kewei Sha, Yunhe Feng on X · view source

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