The Mind AI

Jetson T4000 is positioned as a “physical AI” module: high AI throughput, tight power budgets, and practical edge software. NVIDIA introduced the Jetson T4000 as part of the Jetson Thor family—aimed at robotics and edge AI where power, thermal headroom, and real-time behavior matter as much as raw compute. The headline isn’t only performance; it’s what that performance enables on-device: perception, planning, and modern model inference without leaning on the cloud. TL;DR Compute: up to 1200 FP4 TFLOPS for AI workloads. Memory + power: 64GB memory with power configurable between 40W–70W . Software: powered by JetPack 7.1 , including TensorRT Edge-LLM support and Video Codec SDK support on Jetson Thor. Top 10 things to know about NVIDIA Jetson T4000 It’s a Jetson Thor-family module built for “physical AI” Jetson T4000 is positioned for robotics and edge systems that need real-time perception and decision-making unde...

Microsoft has introduced a new Azure AI datacenter site in Atlanta, Georgia, intended to connect with existing locations and AI supercomputers. This facility is part of an initiative to create a large-scale AI superfactory focused on high computing density, raising questions about the implications of such expansion. TL;DR The article reports on Microsoft’s new Azure AI datacenter aimed at extensive AI computing capacity. It discusses challenges related to energy use, environmental impact, and technical complexity in massive AI infrastructure. Alternatives like distributed and edge computing are considered as potential models for balanced AI growth. Architecture of Large-Scale AI Datacenters The design of these AI datacenters involves connecting multiple sites across regions to form a broad network of computing resources. This setup seeks to enhance efficiency and performance by concentrating AI hardware, though it introduces significant technical ...