News from the AI & ML world
DeeperML - #nvidiaai
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@www.nextplatform.com
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AWS News Blog
, AIwire
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Nvidia's latest Blackwell GPUs are rapidly gaining traction in cloud deployments, signaling a significant shift in AI hardware accessibility for businesses. Amazon Web Services (AWS) has announced its first UltraServer supercomputers, which are pre-configured systems powered by Nvidia's Grace CPUs and the new Blackwell GPUs. These U-P6e instances are available in full and half rack configurations and leverage advanced NVLink 5 ports to create large shared memory compute complexes. This allows for a memory domain spanning up to 72 GPU sockets, effectively creating a massive, unified computing environment designed for intensive AI workloads.
Adding to the growing adoption, CoreWeave, a prominent AI cloud provider, has become the first to offer NVIDIA RTX PRO 6000 Blackwell GPU instances at scale. This move promises substantial performance improvements for AI applications, with reports of up to 5.6x faster LLM inference compared to previous generations. CoreWeave's commitment to early deployment of Blackwell technology, including the NVIDIA GB300 NVL72 systems, is setting new benchmarks in rack-scale performance. By combining Nvidia's cutting-edge compute with their specialized AI cloud platform, CoreWeave aims to provide a more cost-efficient yet high-performing alternative for companies developing and scaling AI applications, supporting everything from training massive language models to multimodal inference. The widespread adoption of Nvidia's Blackwell GPUs by major cloud providers like AWS and specialized AI platforms like CoreWeave underscores the increasing demand for advanced AI infrastructure. This trend is further highlighted by Nvidia's recent milestone of becoming the world's first $4 trillion company, a testament to its leading role in the AI revolution. Moreover, countries like Indonesia are actively pursuing sovereign AI goals, partnering with companies like Nvidia, Cisco, and Indosat Ooredoo Hutchison to establish AI Centers of Excellence. These initiatives aim to foster localized AI research, develop local talent, and drive innovation, ensuring that nations can harness the power of AI for economic growth and digital independence. Recommended read:
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@blogs.nvidia.com
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References:
NVIDIA Newsroom
, www.tomshardware.com
NVIDIA is pushing the boundaries of artificial intelligence through advancements in its RTX AI platform and open-source AI models. The RTX AI platform now accelerates the performance of FLUX.1 Kontext, a groundbreaking image generation model developed by Black Forest Labs. This model allows users to guide and refine the image generation process with natural language, simplifying complex workflows that previously required multiple AI models. By optimizing FLUX.1 Kontext for NVIDIA RTX GPUs using the TensorRT software development kit, NVIDIA has enabled faster inference and reduced VRAM requirements for creators and developers.
The company is also expanding its open-source AI offerings, including the reasoning-focused Nemotron models and the Parakeet speech model. Nemotron, built on top of Llama, delivers groundbreaking reasoning accuracy, while the Parakeet model offers blazing-fast speech capabilities. These open-source tools provide enterprises with valuable resources for deploying multi-model AI strategies and leveraging the power of reasoning in real-world applications. According to Joey Conway, Senior Director of Product Management for AI Models at NVIDIA, reasoning is becoming the key differentiator in AI. In addition to software advancements, NVIDIA is enhancing AI supercomputing capabilities through collaborations with partners like CoreWeave and Dell. CoreWeave has deployed the first Dell GB300 cluster, utilizing the Grace Blackwell Ultra Superchip. Each rack delivers 1.1 ExaFLOPS of AI inference performance and 0.36 ExaFLOPS of FP8 training performance, along with 20 TB of HBM3E and 40 TB of total RAM. This deployment marks a significant step forward in AI infrastructure, enabling unprecedented speed and scale for AI workloads. Recommended read:
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Savannah Martin@News - Stability AI
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References:
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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References:
www.tomshardware.com
, Maginative
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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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References:
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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