News from the AI & ML world
DeeperML - #nvidiaai
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Savannah Martin@News - Stability AI
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News - Stability AI
, Rashi Shrivastava
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Nvidia CEO Jensen Huang has publicly disagreed with claims made by Anthropic's chief, Dario Amodei, regarding the potential job displacement caused by artificial intelligence. Amodei suggested that AI could eliminate a significant portion of entry-level white-collar jobs, leading to a sharp increase in unemployment. Huang, however, maintains a more optimistic view, arguing that AI will ultimately create more career opportunities. He criticized Amodei's stance as overly cautious and self-serving, suggesting that Anthropic's focus on AI safety is being used to limit competition and control the narrative around AI development.
Huang emphasized the importance of open and responsible AI development, contrasting it with what he perceives as Anthropic's closed-door approach. He believes that AI technologies should be advanced safely and transparently, encouraging collaboration and innovation. Huang has underscored that fears of widespread job loss are unfounded, anticipating that AI will revolutionize industries and create entirely new roles and professions that we cannot currently imagine. Nvidia is actively working to make AI more accessible and efficient. Nvidia has collaborated with Stability AI to optimize Stable Diffusion 3.5 models using TensorRT, resulting in significantly faster performance and reduced memory requirements on NVIDIA RTX GPUs. These optimizations extend the accessibility of AI tools to a wider range of users, including creative professionals and developers, fostering further innovation and development in the field. This collaboration provides enterprise-grade image generation to users. Recommended read:
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Anton Shilov@tomshardware.com
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www.tomshardware.com
, Maginative
Nvidia CEO Jensen Huang recently highlighted the significant advancements in artificial intelligence, stating that AI capabilities have increased a millionfold in the last decade. Huang attributed this rapid growth to improvements in GPU performance and system scaling. Speaking at London Tech Week, Huang emphasized the "incredible" speed of industry change and also met with U.K. Prime Minister Keir Starmer to discuss integrating AI into national economic planning through strategic investments in infrastructure, talent development, and government-industry collaborations.
Huang also introduced NVIDIA's Earth-2 platform, featuring cBottle, a generative AI model designed to simulate the global climate at kilometer-scale resolution. This innovative model promises faster and more efficient climate predictions by simulating atmospheric conditions at a detailed 5km resolution, utilizing advanced diffusion modeling to generate realistic atmospheric states based on variables like time of day, year, and sea surface temperatures. The cBottle model can compress massive climate simulation datasets, reducing storage requirements by up to 3,000 times for individual weather samples. The key advantage of cBottle lies in its ability to explicitly simulate convection, which drives thunderstorms, hurricanes, and rainfall, instead of relying on simplified equations used in traditional models. This enhances the accuracy of extreme weather event projections, which are often uncertain in coarser-scale models. Furthermore, cBottle can fill in missing or corrupted climate data, correct biases in existing models, and enhance low-resolution data through super-resolution techniques, making high-resolution climate modeling more accessible and efficient. Recommended read:
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Dashveenjit Kaur@TechHQ
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futurumgroup.com
, insideAI News
Nvidia is significantly expanding its "AI Factory" offerings, collaborating with Dell Technologies to power the next wave of AI infrastructure. This includes the development of the next-generation NERSC-10 supercomputer, dubbed Doudna, based on the Nvidia Vera Rubin supercomputer architecture. This new system is designed to accelerate scientific discovery across fields such as fusion energy, astronomy, and life sciences, benefiting around 11,000 researchers. The Doudna supercomputer will be built by Dell and aims to deliver a tenfold increase in scientific output compared to its predecessor, Perlmutter, while significantly improving power efficiency.
The Doudna supercomputer represents a major investment in scientific computing infrastructure and is named after CRISPR pioneer Jennifer Doudna. Unlike traditional supercomputers, Doudna's architecture prioritizes coherent memory access between CPUs and GPUs, enabling efficient data sharing for modern AI-accelerated scientific workflows. This innovative design integrates simulation, machine learning, and quantum algorithm development, areas increasingly defining cutting-edge research. The supercomputer is expected to be deployed in 2026 and is crucial for maintaining American technological leadership in the face of escalating global competition in AI and quantum computing. In addition to expanding AI infrastructure, Nvidia is also addressing the China AI market by working with AMD to develop export rules-compliant chips. This move comes as the U.S. government has restricted the export of certain high-performance GPUs to China. Nvidia CEO Jensen Huang has emphasized the strategic importance of the Chinese market, noting that a significant portion of AI developers reside there. By creating chips that adhere to U.S. trade restrictions, Nvidia aims to continue serving the Chinese market while ensuring that AI development continues on Nvidia's CUDA platform. Recommended read:
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@blogs.nvidia.com
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Nvidia is significantly expanding its AI infrastructure initiatives by introducing NVLink Fusion, a technology that allows for the integration of non-Nvidia CPUs and AI accelerators with Nvidia's GPUs. This strategic move aims to provide customers with more flexible and customizable AI system configurations, broadening Nvidia's reach in the rapidly growing data center market. Key partnerships are already in place with companies like Qualcomm, Fujitsu, Marvell, and MediaTek, as well as design software firms Cadence and Synopsys, to foster a robust and open ecosystem. This approach allows Nvidia to remain central to the future of AI infrastructure, even when systems incorporate chips from other vendors.
Nvidia is also solidifying its presence in Taiwan, establishing a new office complex near Taipei that will serve as its overseas headquarters. The company is collaborating with Foxconn to build an "AI factory" in Taiwan, which will utilize 10,000 Nvidia Blackwell GPUs. This facility is intended to bolster Taiwan's AI infrastructure and support local organizations in adopting AI technologies across various sectors. TSMC, Nvidia's primary chip supplier, plans to leverage this supercomputer for research and development, aiming to develop the next generation of AI chips. Furthermore, Nvidia is working with Taiwan's National Center for High-Performance Computing (NCHC) to develop a new AI supercomputer. This system will feature over 1,700 GPUs, GB200 NVL72 rack-scale systems, and an HGX B300 system based on the Blackwell Ultra platform, all connected via Quantum InfiniBand networking. Expected to launch later this year, the supercomputer promises an eightfold performance increase over its predecessor for AI workloads, providing researchers with enhanced capabilities to advance their projects. Academic institutions, government agencies, and small businesses in Taiwan will be able to apply for access to the supercomputer to accelerate their AI initiatives. Recommended read:
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staff@insideAI News
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Saudi Arabia is making major strides in artificial intelligence, unveiling deals with several leading U.S. technology firms including NVIDIA, AMD, Cisco, and Amazon Web Services. These partnerships are primarily formed through HUMAIN, the AI subsidiary of Saudi Arabia’s Public Investment Fund (PIF), which controls about $940 billion in assets. As part of these collaborations, Saudi Arabia’s Crown Prince Mohammed bin Salman has launched ‘Humain’ with the intent of establishing the kingdom as a global leader in artificial intelligence. This initiative aligns with the Kingdom’s Vision 2030 plan to diversify its economy and reduce dependence on oil revenues.
NVIDIA has partnered with HUMAIN to construct AI factories in Saudi Arabia. The partnership underscores HUMAIN’s mission to position Saudi Arabia as an international AI powerhouse, and will have a projected capacity of up to 500 megawatts. The initial phase includes the deployment of 18,000 NVIDIA GB300 Grace Blackwell AI supercomputers with NVIDIA InfiniBand networking. AMD has also signed an agreement with HUMAIN where the parties will invest up to $10 billion to deploy 500 megawatts of AI compute capacity over the next five years. In addition to chip manufacturers, networking and cloud service providers are also involved. Cisco will partner with HUMAIN AI enterprise to power AI infrastructure and ecosystem growth, with new investments in research, talent, and digital skills. Amazon Web Services (AWS) and HUMAIN plan to invest over $5 billion to build an “AI Zone” in the kingdom, incorporating dedicated AWS AI infrastructure and services. These efforts are supported by the U.S. government easing AI chip export rules to Gulf states, which had previously limited the access of such countries to high-end AI chips. Recommended read:
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Isha Salian@NVIDIA Blog
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developer.nvidia.com
, BigDATAwire
Nvidia is pushing the boundaries of artificial intelligence with a focus on multimodal generative AI and tools to enhance AI model integration. Nvidia's research division is actively involved in advancing AI across various sectors, underscored by the presentation of over 70 research papers at the International Conference on Learning Representations (ICLR) in Singapore. These papers cover a diverse range of topics including generative AI, robotics, autonomous driving, and healthcare, demonstrating Nvidia's commitment to innovation across the AI spectrum. Bryan Catanzaro, vice president of applied deep learning research at NVIDIA, emphasized the company's aim to accelerate every level of the computing stack to amplify the impact and utility of AI across industries.
Research efforts at Nvidia are not limited to theoretical advancements. The company is also developing tools that streamline the integration of AI models into real-world applications. One notable example is the work being done with NVIDIA NIM microservices, which are being leveraged by researchers at the University College London (UCL) Deciding, Acting, and Reasoning with Knowledge (DARK) Lab to benchmark agentic LLM and VLM reasoning for gaming. These microservices simplify the deployment and scaling of AI models, enabling researchers to efficiently handle workloads of any size and customize models for specific needs. Nvidia's NIM microservices are designed to redefine how researchers and developers deploy and scale AI models, offering a streamlined approach to harnessing the power of GPUs. These microservices simplify the process of running AI inference workloads by providing pre-optimized engines such as NVIDIA TensorRT and NVIDIA TensorRT-LLM, which deliver low-latency, high-throughput performance. The microservices also offer easy and fast API integration with standard frontends like the OpenAI API or LangChain for Python environments. Recommended read:
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@techstrong.ai
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Nvidia has unveiled new tools and capabilities designed to streamline AI deployment and enhance efficiency for enterprises. A key component of this release is the general availability of NVIDIA NeMo microservices, empowering companies to construct AI agents that leverage data flywheels to improve employee productivity. These microservices provide an end-to-end platform for developers, allowing them to create and continuously optimize state-of-the-art agentic AI systems through the integration of inference, business, and user feedback data. The company also highlighted the importance of maintaining a constant stream of high-quality inputs to ensure the accuracy, relevance, and timeliness of AI agents.
Nvidia is also introducing the G-Assist plug-in builder, a tool enabling customization of AI on GeForce RTX AI PCs. This plug-in builder expands the functionality of G-Assist by allowing users to add new commands and connect external tools, which can range from large language models to simple functions like controlling music. Developers can use coding languages like JSON and Python to create tools integrated into G-Assist, and they can submit plug-ins for review and potential inclusion in the NVIDIA GitHub repository, making new capabilities available to others. G-Assist can be modified to perform different actions with different LLMs and software, gaming-related or not. Researchers at the University College London (UCL) Deciding, Acting, and Reasoning with Knowledge (DARK) Lab are leveraging NVIDIA NIM microservices in their new game-based benchmark suite, BALROG, designed to evaluate the agentic capabilities of models on challenging, long-horizon interactive tasks. By using NVIDIA NIM, the DARK lab was able to accelerate their benchmarking process, deploying and hosting the DeepSeek-R1 model without needing to do so locally. This showcases the flexibility and efficiency of NIM microservices, which can be deployed across various platforms, including cloud environments, data centers, and local workstations, enabling seamless integration into diverse workflows and handling workloads of any size. Recommended read:
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@the-decoder.com
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Nvidia is significantly expanding its presence in the AI hardware landscape, announcing plans to mass produce AI supercomputers in Texas and shifting more of its AI production to the United States. This initiative includes building its own factories in the U.S., marking a notable shift in strategy. Working with manufacturing partners like Foxconn in Houston and Wistron in Dallas, Nvidia has commissioned over one million square feet of production space. This move is fueled by a planned investment of up to $500 billion in U.S. AI infrastructure over the next four years. Blackwell AI chips have already begun production at TSMC's facilities in Phoenix, Arizona, indicating rapid progress towards realizing these ambitious goals.
Mass production at the Texas plants is expected to ramp up within the next 12 to 15 months. Nvidia's commitment to US-based production involves partnerships with companies like Amkor and SPIL for packaging and testing operations in Arizona, creating a complex AI chip and supercomputer supply chain. These AI supercomputers are seen as engines for a new type of data center designed specifically for AI processing, referred to as "AI factories." Nvidia anticipates the construction of dozens of these "gigawatt AI factories" in the coming years, further solidifying its role in the rapidly expanding AI industry. In addition to its supercomputing endeavors, Nvidia is intensifying its push into the robotics sector with the launch of its Jetson Thor platform, slated for the first half of 2025. This compact computing platform is specifically designed for humanoid robots, positioning Nvidia to capitalize on the potentially lucrative future of the robotics industry. While Google is also active in this space, Nvidia aims to offer a comprehensive technology platform for "physical AI," encompassing both the necessary software and robotic chips. Their existing Jetson Orin series chips have already seen adoption by humanoid robot manufacturers, and the upcoming Jetson Thor promises further enhanced capabilities. Recommended read:
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@the-decoder.com
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Nvidia is making significant advancements in artificial intelligence, showcasing innovations in both hardware and video generation. A new method developed by Nvidia, in collaboration with Stanford University, UCSD, UC Berkeley, and UT Austin, allows for the creation of AI-generated videos up to one minute long. This breakthrough addresses previous limitations in video length, where models like OpenAI's Sora, Meta's MovieGen, and Google's Veo 2 were capped at 20, 16, and 8 seconds respectively.
The key innovation lies in the introduction of Test-Time Training layers (TTT layers), which are integrated into a pre-trained Transformer architecture. These layers replace simple hidden states in conventional Recurrent Neural Networks (RNNs) with small neural networks that continuously learn during the video generation process. This allows the system to maintain consistency across longer sequences, ensuring elements like characters and environments remain stable throughout the video. This new method has even been showcased with an AI-generated "Tom and Jerry" cartoon. Furthermore, Nvidia has unveiled its new Llama-3.1 Nemotron Ultra large language model (LLM), which outperforms DeepSeek R1 despite having less than half the parameters. The Llama-3.1-Nemotron-Ultra-253B is a 253-billion parameter model designed for advanced reasoning, instruction following, and AI assistant workflows. Its architecture includes innovations such as skipped attention layers, fused feedforward networks, and variable FFN compression ratios. The model's code is publicly available on Hugging Face, reflecting Nvidia's commitment to open-source AI development. Recommended read:
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