Quartet: Native FP4 Training Can Be Optimal for Large Language Models

NVIDIA's Learn-to-Steer is set to address a significant limitation in text-to-image diffusion models, which struggle with basic spatial reasoning. These models can create photorealistic images but often misplace objects in relation to one another, such as placing a dog to the left of a teddy bear instead of the right. This advancement aims to enhance the accuracy of generated images by improving spatial understanding.

As artificial intelligence transforms industries, universities must adapt or risk obsolescence. Institutions that embrace AI across various fields, enhance their computing infrastructure, and engage in innovative research will attract top talent and funding. This industry brief outlines a strategic framework for developing a comprehensive AI strategy that boosts enrollment and prepares graduates for future careers.

AI is revolutionizing various industries, but effective infrastructure is crucial for optimal performance. This ebook outlines strategies to enhance AI workloads, including optimizing infrastructure for applications like chatbots, utilizing dynamic batching and KV caching to reduce costs, and leveraging technologies like NVIDIA GPUs and Kubernetes for scalability.

The paper titled 'ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels' addresses the challenges of inter-GPU communication, which has become a significant bottleneck for modern AI workloads. As AI models scale, the disparity between hardware compute throughput and interconnect bandwidth has widened. The authors propose ParallelKittens (PK), a minimal CUDA framework designed to simplify the development of overlapped multi-GPU kernels. PK builds on the ThunderKittens framework and introduces eight core primitives and a unified programming template to enhance multi-GP…

Microsoft and NVIDIA have announced plans to invest up to $15 billion in Anthropic, an AI safety and research company. This investment aims to enhance the development of advanced AI technologies while ensuring safety and alignment with human values. The collaboration is expected to leverage Anthropic's expertise in AI safety to create more robust and responsible AI systems.

Microsoft and NVIDIA have announced a joint investment of up to $15 billion in Anthropic, an AI safety and research company. This investment is part of a broader strategy to enhance the development of advanced AI technologies. Anthropic has also committed to purchasing $30 billion in Azure compute capacity, with the option for additional resources, indicating a significant partnership aimed at advancing AI capabilities while ensuring safety in deployment.

The article discusses the development of a portable and efficient large language model (LLM) inference platform using a state-of-the-art paged attention kernel. This kernel, built on the Triton language, aims to enhance performance on both NVIDIA and AMD GPUs without requiring low-level hand-tuning. The authors detail their approach, algorithmic improvements, and the necessary auto-tuning for efficiency, achieving a significant performance increase from 19.7% to a much higher benchmark.

The paper titled 'Optimizing Mixture of Block Attention' by Lu et al. (2025) discusses the Mixture of Block Attention (MoBA) as a significant advancement for processing long contexts in large language models (LLMs). MoBA allows queries to selectively attend to a limited number of key-value blocks, which significantly reduces computational costs. However, the authors note that the principles affecting MoBA's performance are not well understood, and the absence of an efficient GPU implementation limits its practical use. The study introduces a statistical model to analyze MoBA's mechanics, empha…