It seems like things are getting exciting in the world of inZOI once again, after what felt like months of no comms and some patch delays. Earlier this week, the team behind the life sim finally announced a release date for its next update. Read mor

AI reasoning, inference and networking will be top of mind for attendees of next week’s Hot Chips conference. A key forum for processor and system architects from industry and academia, Hot Chips — running Aug. 24-26 at Stanford University — showcases the latest innovations poised to advance AI factories and drive revenue for the trillion-dollar data center computing market. At the conference, NVIDIA will join industry leaders including Google and Microsoft in a “tutorial” session — taking place on Sunday, Aug. 24 — that discusses designing rack-scale architecture for data centers. In addition, NVIDIA experts will present at four sessions and one tutorial detailing how: NVIDIA networking, including the NVIDIA ConnectX-8 SuperNIC, delivers AI reasoning at rack- and data-center scale. (Featuring Idan Burstein, principal architect of network adapters and systems-on-a-chip at NVIDIA) Neural rendering advancements and massive leaps in inference — powered by the NVIDIA Blackwell architecture, including the NVIDIA GeForce RTX 5090 GPU — provide next-level graphics and simulation capabilities. (Featuring Marc Blackstein, senior director of architecture at NVIDIA) Co-packaged optics (CPO) switches with integrated silicon photonics — built with light-speed fiber rather than copper wiring to send information quicker and using less power — enable efficient, high-performance, gigawatt-scale AI factories. The talk will also highlight NVIDIA Spectrum-XGS Ethernet, a new scale-across technology for unifying distributed data centers into AI super-factories. (Featuring Gilad Shainer, senior vice president of networking at NVIDIA) The NVIDIA GB10 Superchip serves as the engine within the NVIDIA DGX Spark desktop supercomputer. (Featuring Andi Skende, senior distinguished engineer at NVIDIA) It’s all part of how NVIDIA’s latest technologies are accelerating inference to drive AI innovation everywhere, at every scale. NVIDIA Networking Fosters AI Innovation at Scale AI reasoning — when artificial intelligence systems can analyze and solve complex problems through multiple AI inference passes — requires rack-scale performance to deliver optimal user experiences efficiently. In data centers powering today’s AI workloads, networking acts as the central nervous system, connecting all the components — servers, storage devices and other hardware — into a single, cohesive, powerful computing unit. NVIDIA ConnectX-8 SuperNIC Burstein’s Hot Chips session will dive into how NVIDIA networking technologies — particularly NVIDIA ConnectX-8 SuperNICs — enable high-speed, low-latency, multi-GPU communication to deliver market-leading AI reasoning performance at scale. As part of the NVIDIA networking platform, NVIDIA NVLink, NVLink Switch and NVLink Fusion deliver scale-up connectivity — linking GPUs and compute elements within and across servers for ultra low-latency, high-bandwidth data exchange. NVIDIA Spectrum-X Ethernet provides the scale-out fabric to connect entire clusters, rapidly streaming massive datasets into AI models and orchestrating GPU-to-GPU communication across the data center. Spectrum-XGS Ethernet scale-across technology extends the extreme performance and scale of Spectrum-X Ethernet to interconnect multiple, distributed data centers to form AI super-factories capable of giga-scale intelligence. Connecting distributed AI data centers with NVIDIA Spectrum-XGS Ethernet. At the heart of Spectrum-X Ethernet, CPO switches push the limits of performance and efficiency for AI infrastructure at scale, and will be covered in detail by Shainer in his talk. NVIDIA GB200 NVL72 — an exascale computer in a single rack — features 36 NVIDIA GB200 Superchips, each containing two NVIDIA B200 GPUs and an NVIDIA Grace CPU, interconnected by the largest NVLink domain ever offered, with NVLink Switch providing 130 terabytes per second of low-latency GPU communications for AI and high-performance computing workloads. An NVIDIA rack-scale system. Built with the NVIDIA Blackwell architecture, GB200 NVL72 systems deliver massive leaps in reasoning inference performance. NVIDIA Blackwell and CUDA Bring AI to Millions of Developers The NVIDIA GeForce RTX 5090 GPU — also powered by Blackwell and to be covered in Blackstein’s talk — doubles performance in today’s games with NVIDIA DLSS 4 technology. NVIDIA GeForce RTX 5090 GPU It can also add neural rendering features for games to deliver up to 10x performance, 10x footprint amplification and a 10x reduction in design cycles,  helping enhance realism in computer graphics and simulation. This offers smooth, responsive visual experiences at low energy consumption and improves the lifelike simulation of characters and effects. NVIDIA CUDA, the world’s most widely available computing infrastructure, lets users deploy and run AI models using NVIDIA Blackwell anywhere. Hundreds of millions of GPUs run CUDA across the globe, from NVIDIA GB200 NVL72 rack-scale systems to GeForce RTX– and NVIDIA RTX PRO-powered PCs and workstations, with NVIDIA DGX Spark powered by NVIDIA GB10 — discussed in Skende’s session — coming soon. From Algorithms to AI Supercomputers — Optimized for LLMs NVIDIA DGX Spark Delivering powerful performance and capabilities in a compact package, DGX Spark lets developers, researchers, data scientists and students push the boundaries of generative AI right at their desktops, and accelerate workloads across industries. As part of the NVIDIA Blackwell platform, DGX Spark brings support for NVFP4, a low-precision numerical format to enable efficient agentic AI inference, particularly of large language models (LLMs). Learn more about NVFP4 in this NVIDIA Technical Blog. Open-Source Collaborations Propel Inference Innovation NVIDIA accelerates several open-source libraries and frameworks to accelerate and optimize AI workloads for LLMs and distributed inference. These include NVIDIA TensorRT-LLM, NVIDIA Dynamo, TileIR, Cutlass, the NVIDIA Collective Communication Library and NIX — which are integrated into millions of workflows. Allowing developers to build with their framework of choice, NVIDIA has collaborated with top open framework providers to offer model optimizations for FlashInfer, PyTorch, SGLang, vLLM and others. Plus, NVIDIA NIM microservices are available for popular open models like OpenAI’s gpt-oss and Llama 4,  making it easy for developers to operate managed application programming interfaces with the flexibility and security of self-hosting models on their preferred infrastructure. Learn more about the latest advancements in inference and accelerated computing by joining NVIDIA at Hot Chips.  

Japan is once again building a landmark high-performance computing system — not simply by chasing speed, but by rethinking how technology can best serve the nation’s most urgent scientific needs. At the FugakuNEXT International Initiative Launch Ceremony held in Tokyo on Aug. 22, leaders from RIKEN, Japan’s top research institute, announced the start of an international collaboration with Fujitsu and NVIDIA to co-design FugakuNEXT, the successor to the world-renowned supercomputer, Fugaku. Awarded early in the process, the contract enables the partners to work side by side in shaping the system’s architecture to address Japan’s most critical research priorities — from earth systems modeling and disaster resilience to drug discovery and advanced manufacturing. More than an upgrade, the effort will highlight Japan’s embrace of modern AI and showcase Japanese innovations that can be harnessed by researchers and enterprises across the globe. The ceremony featured remarks from the initiative’s leaders, RIKEN President Makoto Gonokami and Satoshi Matsuoka, director of the RIKEN Center for Computational Science and one of Japan’s most respected high-performance computing architects. Fujitsu Chief Technology Officer Vivek Mahajan attended, emphasizing the company’s role in advancing Japan’s computing capabilities. Ian Buck, vice president of hyperscale and high-performance computing at NVIDIA, attended in person as well to discuss the collaborative design approach and how the resulting platform will serve as a foundation for innovation well into the next decade. Momentum has been building. When NVIDIA founder and CEO Jensen Huang touched down in Tokyo last year, he called on Japan to seize the moment — to put NVIDIA’s latest technologies to work building its own AI, on its own soil, with its own infrastructure. FugakuNEXT answers that call, drawing on NVIDIA’s whole software stack —  from NVIDIA CUDA-X libraries such as NVIDIA cuQuantum for quantum simulation, RAPIDS for data science, NVIDIA TensorRT for high-performance inference and NVIDIA NeMo for large language model development, to other domain-specific software development kits tailored for science and industry. Innovations pioneered on FugakuNEXT could become blueprints for the world. What’s Inside FugakuNEXT will be a hybrid AI-HPC system, combining simulation and AI workloads. It will feature FUJITSU-MONAKA-X CPUs, which can be paired with NVIDIA technologies using NVLink Fusion, new silicon enabling high-bandwidth connections between Fujitsu’s CPUs and NVIDIA’s architecture. The system will be built for speed, scale and efficiency. What It Will Do FugakuNEXT will support a wide range of applications — such as automating hypothesis generation, code creation and experiment simulation. Scientific research: Accelerating simulations with surrogate models and physics-informed neural networks. Manufacturing: Using AI to learn from simulations to generate efficient and aesthetically pleasing designs faster than ever before. Earth systems modeling: aiding disaster preparedness and prediction for earthquakes and severe weather, and more. RIKEN, Fujitsu and NVIDIA will collaborate on software developments, including tools for mixed-precision computing, continuous benchmarking, and performance optimization. FugakuNEXT isn’t just a technical upgrade — it’s a strategic investment in Japan’s future. Backed by Japan’s MEXT (Ministry of Education, Culture, Sports, Science and Technology), it will serve universities, government agencies, and industry partners nationwide. It marks the start of a new era in Japanese supercomputing — one built on sovereign infrastructure, global collaboration, and a commitment to scientific leadership. Image courtesy of RIKEN

Welcome to the Clojure Deref! This is a weekly link/news roundup for the Clojure ecosystem. (@ClojureDeref RSS) Podcasts and videos Live-editable Physics Simulation - Sam Ritchie FlowStorm searching and following values - Juan Monetta

Honestly, there's no such thing as too many interactive browser-based simulations. Software Engineer Michal Zalobny has created a cloth simulation from scratch, rendered using his custom WebGL2 engine.To boost performance, all points and joints are instanced, and Michal has applied quaternion math to accurately define the orientation of the joints. He was inspired by an article by Marian Pekár on how to use Verlet integration to write a simple 2D cloth simulation with C++.Give it a try here, and have a look at Claudio Z.'s similar cloth simulation that tears when dragged by the mouse:Of course, Claudio Z. has also made his project available online:Join our 80 Level Talent platform and our new Discord server, follow us on Instagram, Twitter, LinkedIn, Telegram, TikTok, and Threads, where we share breakdowns, the latest news, awesome artworks, and more.