{"slug": "approximating-softmax-in-pretrained-llms-model-sensitivity-and-kernel", "title": "Approximating Softmax in Pretrained LLMs: Model Sensitivity and Kernel Acceleration", "summary": "On NVIDIA Blackwell B200 GPUs, tensor-core throughput exceeds special-function exponential throughput by more than two orders of magnitude, exposing exponential evaluation as a bottleneck in fused attention kernels, according to research characterizing softmax approximation in pretrained LLMs. The work examines model sensitivity to approximating softmax and kernel acceleration, noting that a pretrained Transformer may not need the exponential evaluated accurately at every element.", "body_md": "On NVIDIA Blackwell B200, tensor-core throughput outpaces special-function exponential throughput by more than two orders of magnitude, exposing exponential evaluation in fused attention kernels. A pretrained Transformer, however, may not need it evaluated accurately at every element. We characteriz", "url": "https://wpnews.pro/news/approximating-softmax-in-pretrained-llms-model-sensitivity-and-kernel", "canonical_source": "https://aiflash.com/news/128945/", "published_at": "2026-09-30 01:00:31+00:00", "updated_at": "2026-09-30 01:17:57.866622+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-infrastructure", "ai-chips", "machine-learning"], "entities": ["NVIDIA", "Blackwell B200", "Transformer"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/approximating-softmax-in-pretrained-llms-model-sensitivity-and-kernel", "markdown": "https://wpnews.pro/news/approximating-softmax-in-pretrained-llms-model-sensitivity-and-kernel.md", "text": "https://wpnews.pro/news/approximating-softmax-in-pretrained-llms-model-sensitivity-and-kernel.txt", "jsonld": "https://wpnews.pro/news/approximating-softmax-in-pretrained-llms-model-sensitivity-and-kernel.jsonld"}}