The Mind AI

AI usage keeps expanding, and so does the demand for tokens (the units generated by language models). When usage grows, the winning platform is often the one that can generate more tokens per second without exploding cost and power. That is where Mixture of Experts (MoE) models and NVIDIA’s Blackwell platform intersect. Note: This article is informational only and not purchasing or engineering advice. Performance depends on model, sequence length, batching, and software versions. Platform capabilities can change over time. TL;DR Token throughput is the bottleneck for scaled AI services: more tokens per second usually means lower cost per answer. MoE models activate only a subset of parameters per token, improving efficiency while keeping model capacity high. Blackwell + inference software focuses on faster expert routing, better all-to-all communication, and low-precision execution to lift MoE throughput. Skim Guide MoE basic...

NVIDIA Cosmos Reason 2 is positioned as a reasoning-focused vision-language model (VLM) aimed at “physical AI” use cases, where an agent must interpret images or video, understand how the world changes over time, and choose plausible next steps. The goal is not only better perception, but better planning-style outputs that are useful in robotics, autonomous systems, and simulation-heavy workflows. Note: This post is informational only and not safety, engineering, or compliance advice. Physical AI systems can cause real-world harm if misused or misconfigured. Capabilities and deployment practices can change over time. TL;DR Cosmos Reason 2 is a reasoning VLM for robotics and physical AI that focuses on space + time understanding , not just static image recognition. It adds features geared toward workflow outputs such as 2D/3D point localization , bounding box coordinates , and much longer context windows (up to 256K input tokens ). The hardest prob...

NVIDIA’s founder and CEO, Jensen Huang, opened CES 2026 in Las Vegas with a single, sweeping idea: AI is no longer confined to the data center. It’s becoming the default way software is built, delivered, and experienced—across enterprise platforms, autonomous systems, and everyday devices. In his view, accelerated computing is “modernizing” a massive portion of recent computing investment, reframing GPUs as the engine of a new era. Note: This post is informational only and not financial, legal, or engineering advice. Performance claims depend on model, workload, configuration, and software versions. Products, rollouts, and policies can change over time. TL;DR NVIDIA’s CES 2026 message is that accelerated computing is reshaping how software runs and how AI scales across industries. The company introduced Rubin , a six-chip platform designed as a rack-scale AI supercomputer approach that aims to reduce bottlenecks and lower training and inference costs. ...

Large language models (LLMs) frequently process long sequences of text, known as long-contexts, to support tasks like document analysis and conversational understanding. However, increasing the length of input context leads to a substantial rise in computational demands for the attention mechanism, which can affect the efficiency of AI deployment. TL;DR The article reports that attention computation grows quadratically with input length, increasing resource use significantly. Techniques like skip softmax in NVIDIA TensorRT-LLM reduce unnecessary calculations during inference. Enhancing attention efficiency may help balance AI performance with societal and environmental considerations. Challenges of Long-Context Processing in AI LLMs rely on attention mechanisms to evaluate the relevance of tokens within long input sequences. As the context length increases, the required computations for attention grow rapidly, often quadratically. This escalation ...

Artificial intelligence development relies on three main scaling laws: pre-training, post-training, and test-time scaling. These principles help explain how AI models improve in capability and efficiency, influencing automation and workflow optimization. TL;DR The text says pre-training builds broad AI knowledge, enabling flexible workflows. The article reports post-training tailors AI to specific tasks, enhancing precision. Test-time scaling allows dynamic adjustments for real-time workflow optimization. Understanding AI Scaling Laws Scaling laws describe how AI models evolve through stages that impact their performance and adaptability. These stages guide improvements that support automation by enabling smarter and more efficient task handling. Pre-Training as the Base Layer Pre-training involves exposing AI models to extensive datasets to develop general understanding before task-specific use. This foundation allows AI to manage varied inputs...

The NVIDIA Blackwell architecture has shown notable performance across all MLPerf Training v5.1 benchmarks. These benchmarks assess the speed and efficiency of training machine learning models, which are key factors in automation and AI-driven workflows. TL;DR The article reports NVIDIA Blackwell’s strong results on MLPerf Training v5.1 benchmarks. Faster training speeds can influence the adaptability of automated machine learning workflows. Increasing model complexity demands efficient architectures to maintain training performance. Overview of NVIDIA Blackwell and MLPerf Training Benchmarks The NVIDIA Blackwell architecture has recently demonstrated leading training speeds in MLPerf Training v5.1. These benchmarks provide a standardized measure of how quickly and efficiently machine learning models can be trained, which is important for workflows relying on AI automation. The Role of Training Speed in Machine Learning Automation Training speed...