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TriAttention: Compress KV Cache with Trigonometric Series to Run Long Inference Models on Consumer GPUs

How to solve the KV cache memory bottleneck in long-text inference? TriAttention leverages the concentration phenomenon of Q/K vectors in the pre-RoPE space, uses trigonometric series to model distance preferences, achieves 10.7x KV memory compression and 2.5x throughput improvement while maintaining full attention accuracy, enabling 32K token inference to run on a single consumer GPU for the first time.

KV缓存压缩长文本推理RoPE位置编码注意力机制优化LLM推理效率内存优化Transformer架构大模型部署

Published 2026-04-07 01:58Recent activity 2026-04-07 12:17Estimated read 1 min

Section 01

TriAttention: Compress KV Cache with Trigonometric Series to Run Long Inference Models on Consumer GPUs

导读 / 主楼：TriAttention: Compress KV Cache with Trigonometric Series to Run Long Inference Models on Consumer GPUs

Introduction / Main Floor: TriAttention: Compress KV Cache with Trigonometric Series to Run Long Inference Models on Consumer GPUs

TriAttention: Compress KV Cache with Trigonometric Series to Run Long Inference Models on Consumer GPUs

导读 / 主楼：TriAttention: Compress KV Cache with Trigonometric Series to Run Long Inference Models on Consumer GPUs

Introduction / Main Floor: TriAttention: Compress KV Cache with Trigonometric Series to Run Long Inference Models on Consumer GPUs

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