AdaMX puts per-block format and enhancement metadata into spare MX exponent bits, recovering 83% of MXFP4's commonsense accuracy loss at the same 4-bit storage budget.
Tender: Accelerating large language models via tensor decomposition and runtime requantization,
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Heterogeneity-Aware Microscaling for Efficient Low-Bit LLM Inference
AdaMX puts per-block format and enhancement metadata into spare MX exponent bits, recovering 83% of MXFP4's commonsense accuracy loss at the same 4-bit storage budget.