去年7月，FlashAttention-2发布，相比第一代实现了2倍的速度提升，比PyTorch上的标准注意力操作快5～9倍，达到A100上理论最大FLOPS的50～73%，实际训练速度可达225 TFLOPS（模型FLOPs利用率为72%）。

This study provides a useful application of computational modelling to examine how people with chronic pain learn under uncertainty, contributing to efforts to link pain with motivational processes.

Machine learning is reshaping the way portfolios are built, monitored, and adjusted. Investors are no longer limited to ...

In deep learning, classification models don’t just need to make predictions—they need to express confidence. That’s where the Softmax activation function comes in. Softmax takes the raw, unbounded ...

NVIDIA's Skip Softmax in TensorRT-LLM offers up to 1.4x faster inference for LLMs by optimizing attention computation, enhancing performance on Hopper and Blackwell architectures. NVIDIA has unveiled ...

NVIDIA has unveiled a new technique called Skip Softmax, integrated into its TensorRT-LLM, which promises to accelerate long-context inference. This development comes as a response to the increasingly ...

The ability to generate accurate conclusions based on data inputs is essential for strong reasoning and dependable performance in Artificial Intelligence (AI) systems. The softmax function is a ...