LoKA: Low-precision Kernel Applications for Recommendation Models At Scale

arXiv:2605.10886v3 Announce Type: replace Abstract: Recent GPU generations deliver significantly higher FLOPs using lower-precision arithmetic, such as FP8. While successfully applied to large language models (LLMs), its adoption in large recommendation models (LRMs) has been limited. This is because LRMs are numerically sensitive, dominated by small matrix multiplications (GEMMs) followed by normalization, and trained in communication-intensive environments. Applying FP8 directly to LRMs often degrades model quality and prolongs training time. These challenges are inherent to LRM workloads an

Source: arXiv cs.LG — read the full report at the original publisher.

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