MLAfication: Efficiently Enabling High-Fidelity MLA for Modern LLMs

Abstract:Multi-Head Latent Attention (MLA) compresses the KV cache into a latent representation, reducing inference memory and bandwidth. Existing MHA2MLA/TransMLA methods do not support QKNorm and require costly recovery on modern LLMs, especially MoE. We present MLAfication, a QKNorm-compatible, parameter-efficient extension of MHA2MLA. It combines calibrated KNorm, parameter-efficient distillation, contribution-aware Partial-RoPE, and activation-aware joint KV initialization. Across MHA and GQA models, including QKNorm- and MoE-equipped variants, MLAfication preserves performance under substantial KV-cache compression, surpasses the current SOTA on commonsense and long-context benchmarks, and extends MLA conversion to modern architectures. On Qwen3-30B-A3B, MLAfication delivers up to $5.35\times$ the serving throughput of BF16 GQA and, combined with 4-bit KV quantization, reduces the KV-cache footprint by $94.73\%$. Our code is available at this https URL.

Machine Learning (cs.LG); Computation and Language (cs.CL)

arXiv:2610.17202 [cs.LG]

(or arXiv:2610.17202v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.17202 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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