Nvidia

NVIDIA and AWS have collaborated to bring AI to production at scale, addressing constraints such as low-latency inference, fast vector search, and strong GPU price-performance. The NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs power new Amazon EC2 G7 instances, delivering up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6 instances. The NVIDIA cuVS library accelerates the retrieval layer by making GPU-powered vector indexing the default in OpenSearch Serverless, resulting in vector indexing up to 10x faster at a quarter of the cost. This collaboration provides enterprises with practical paths to deploy AI at production scale, enabling lower-latency inference and faster vector search.

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The NVIDIA Agent Toolkit provides a foundation for building specialized AI agents that can be customized, controlled, and trusted by enterprises and developers. This toolkit includes models, tools, skills, and a secure runtime, enabling the creation of digital AI coworkers that can reason, use tools, and take action. With the NVIDIA Agent Toolkit, businesses can build specialized AI agents that fit their specific workflows, leading to increased efficiency and productivity. The practical implication for engineers building AI systems is that they can now create customized AI agents that can be integrated into existing systems and workflows.

NVIDIA technologies power over 400 of the world's 500 fastest supercomputers, with 81% of the TOP500 and 90% of new systems on the list utilizing NVIDIA technology. The top eight systems on the Green500 run on NVIDIA GPUs, with the No. 1 system, KAIROS, using a single NVIDIA Grace Hopper Superchip to achieve 73.3 gigaflops per watt. NVIDIA's momentum in new deployments is driven by a preference for machines built for AI, simulation, and science, with NVIDIA systems delivering more than 2x the AI training and nearly 3x the AI inference throughput of every other platform combined. This trend has significant implications for engineers building AI systems, as accelerated computing becomes the foundation for systems tackling demanding workloads.

NVIDIA is bringing trusted, 24/7 AI agents to telecom operations, enabling autonomous networks and operations where AI agents proactively watch for problems and coordinate changes across network, IT, and business systems. The company is demonstrating the building blocks of a secure, telecom autonomy platform, including synthetic data, telecom-domain models, secure agent runtimes, and simulations. This platform allows agents to understand operator intent, act safely across business and network domains, and keep humans in control of policy. The practical implication for engineers building AI systems is the ability to create more autonomous, resilient networks and power richer AI-driven services for consumers and businesses.

The introduction of NVIDIA Blackwell GPUs on Amazon SageMaker AI enables the optimization of model training for large AI models by reducing constraints such as batch sizes limited by GPU memory and sequence lengths cut short to avoid out-of-memory errors. With Blackwell's expanded memory and new precision formats, users can train models with larger batch sizes, longer sequence lengths, and reduced model sharding, resulting in improved throughput and reduced communication overhead. The use of PyTorch Fully Sharded Data Parallel (FSDP) and strategic application of activation checkpointing can further optimize training configurations. This leads to faster iteration cycles, less networking overhead, and lower infrastructure costs. By properly configuring Blackwell training jobs, users can process larger batch sizes without aggressive sharding and achieve better results for long-range depende

The National Artificial Intelligence Research Resource (NAIRR) pilot program, powered by NVIDIA AI infrastructure, has successfully driven over 700 innovative research projects across the U.S. in the past two years, with applications in protein prediction and infectious disease outbreak management. This program leverages NVIDIA's AI capabilities to accelerate scientific research, demonstrating the potential of AI in driving breakthroughs in various fields. By harnessing the power of AI infrastructure, researchers can now focus on complex scientific problems, leading to more efficient and effective discovery processes. This initiative has the potential to revolutionize the way scientific research is conducted, enabling faster and more accurate results.

NVIDIA has integrated its Vera CPU into Los Alamos National Laboratory's (LANL) new supercomputers, leveraging HPE's Cray Supercomputing GX5000 architecture to accelerate scientific discovery and unlock agentic AI for science. The Vera CPU is designed to provide a significant boost in performance and efficiency, enabling researchers to tackle complex scientific problems. This integration marks a significant step towards the development of autonomous, agentic AI systems that can drive scientific innovation. The resulting supercomputers will be capable of processing vast amounts of data and executing complex tasks, driving breakthroughs in fields such as physics, chemistry, and materials science.

NVIDIA has introduced the DAQIRI library and ALCHEMI NIM microservices, accelerating AI for scientific discoveries in fields like chemistry, materials science, and astronomy. The new software leverages NVIDIA's cuPhoton reference code and can be used for tasks such as materials simulation and experimental astronomy. This technology has the potential to unlock groundbreaking discoveries, but its adoption may be limited by the complexity of integrating it with existing research pipelines. The DAQIRI library and ALCHEMI NIM microservices are designed to be highly scalable and can be easily integrated into large-scale scientific simulations.

Nvidia is betting on agentic AI to accelerate biotech discovery, as announced at the Bio International Convention in San Diego. The company's vice president and general manager of healthcare and life sciences, Kimberly Powell, made the case for agentic AI in a special address. Not mentioned are specific numbers, model names, or benchmark results. The practical implication for engineers building AI systems is the potential application of agentic AI in biotech discovery. Agentic AI may enable more efficient and effective discovery processes.

Nvidia and DDN have introduced a joint solution to address the economic challenges of AI infrastructure, leveraging their combined expertise in data and compute to optimize performance and reduce costs. Their partnership aims to enable enterprises to extract maximum value from their AI investments by streamlining data movement and processing. This joint solution is designed to handle massive amounts of data and scale with growing AI workloads, making it an attractive option for large-scale AI deployments. By combining Nvidia's high-performance GPUs with DDN's storage solutions, the partnership has achieved significant performance improvements and cost reductions, setting a new standard for AI infrastructure economics.

NVIDIA's newest AI servers can run their cooling liquid at up to 45 degrees Celsius, making them more energy efficient and achieving 100% liquid cooling with no fans in the system. The Rubin generation of NVIDIA AI infrastructure is the first to achieve this, and it is outlined in the NVIDIA DSX AI factory reference design. This liquid cooling methodology enables data centers to reduce cooling energy consumption, making a significant difference in overall data center energy use. The practical implication for engineers building AI systems is that they can design more efficient and sustainable data centers using liquid-cooled infrastructure.

France has successfully deployed AI infrastructure, leveraging NVIDIA technologies to establish national compute capacity and enable the development of open frontier models and industrial platforms, with AI agents now running in production. This marks a significant milestone in advancing Europe's AI future. The deployment combines NVIDIA's AI expertise with France's strategic investment, fostering innovation and driving economic growth. This achievement serves as a model for other European countries to follow, demonstrating the potential of collaborative efforts between governments and tech giants.

NVIDIA XR AI is now available in public beta, providing a framework for developers to build multimodal AI agents for AR glasses and XR devices. This framework enables the creation of agents that can interact with users in a hands-free manner. Not mentioned are specific numbers, model names, or benchmark results. The practical implication for engineers building AI systems is the ability to create more interactive and immersive experiences for users. The public beta release of NVIDIA XR AI allows developers to start building and testing their own multimodal AI agents.