AI BriefingTriAttention for KV Cache Compression (GitHub Repository)
Key point
TriAttention compresses the KV cache by 10.7x and improves throughput by 2.5x through trigonometric-based compression without loss of accuracy.
Details
TriAttention leverages trigonometric frequency-domain compression to solve the KV cache problem that arises during long reasoning tasks.
Unlike existing attention-based methods, TriAttention scores keys using the centroid and norm of pre-RoPE Q/K vectors, without the overhead of separately selecting representative queries. This enables highly efficient compression while maintaining accuracy.
Key performance metrics are as follows:
- 10.7x KV memory savings
- 2.5x throughput improvement (based on the AIME25 benchmark)
- Accuracy maintained equal to Full Attention
It also supports Apple Silicon (MLX), AMD GPU (llama.cpp), and vLLM, and is compatible with OpenClaw, enabling smooth deployment even in local environments such as an RTX 4090 (24GB).
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