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
The continuous drive for efficiency in AI compute, especially as Large Recommendation Models (LRMs) scale, necessitates innovations beyond Large Language Models (LLMs) to leverage advanced hardware capabilities.
Improving the efficiency of LRMs through technologies like FP8 is crucial for sustainable AI infrastructure growth, impacting operational costs and the scalability of personalization services.
The explicit recognition and proposed solution for the numerical sensitivity of LRMs when using low-precision arithmetic signals a potential breakthrough for broader adoption of GPU-accelerated computing for these models.
- · GPU manufacturers
- · Hyperscalers (cloud providers)
- · AI software developers focusing on recommendation systems
- · E-commerce and social media platforms
- · Companies with less efficient recommendation infrastructure
- · Developers stuck on higher-precision compute
Recommendation models will become more cost-effective to train and deploy, accelerating their complexity and ubiquity.
Increased efficiency in LRMs could further democratize access to advanced personalization and recommendation technologies, expanding their application across industries.
The reduced compute cost per recommendation could lead to a 'recommendation-first' paradigm in many digital interactions, fundamentally altering user experience and content discovery.
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Read at arXiv cs.LG