News from the AI & ML world
DeeperML - #nvidia
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Allyson Vasquez@NVIDIA Technical Blog
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NVIDIA's GTC 2025 is shaping up to be a major event for AI enthusiasts, packed with networking opportunities, live demos, and discussions on the latest AI innovations. Data Phoenix is highlighting the event as a key gathering, featuring meetups, networking receptions, and hands-on sessions alongside the main conference. They are also co-hosting and supporting key events like the INFRA@GTC Networking Reception and AI Demo Jam.
VAST Data plans to showcase its data platform for enterprise Retrieval Augmented Generation (RAG) use cases at the conference. Microsoft and NVIDIA have also announced a partnership to integrate RTX Neural Shaders into a DirectX preview in April, bringing more AI capabilities to game development. This integration will allow developers to leverage Tensor cores in RTX GPUs to accelerate neural networks within a game's graphics pipeline. Recommended read:
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Jaime Hampton@BigDATAwire
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NVIDIA's GTC 2025 showcased significant advancements in AI, marked by the unveiling of the Blackwell Ultra GPU and the Vera Rubin roadmap extending through 2027. CEO Jensen Huang emphasized a 40x AI performance leap with the Blackwell platform compared to its predecessor, Hopper, highlighting its crucial role in inference workloads. The conference also introduced open-source ‘Dynamo’ software and advancements in humanoid robotics, demonstrating NVIDIA’s commitment to pushing AI boundaries.
The Blackwell platform is now in full production, meeting incredible customer demand, and the Vera Rubin roadmap details the next generation of superchips expected in 2026. Huang also touted new DGX systems, highlighting the push towards photonic switches to handle growing data demands efficiently. Blackwell Ultra will offer 288GB of memory. NVIDIA claims the GB300 chip brings 1.5x more AI performance than the NVIDIA GB200. These advancements aim to bolster AI reasoning capabilities and energy efficiency, positioning NVIDIA to maintain its dominance in AI infrastructure. Recommended read:
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Noah Kravitz@NVIDIA Blog
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NVIDIA is making strides in both agentic AI and open-source initiatives. Jacob Liberman, director of product management at NVIDIA, explains how agentic AI bridges the gap between powerful AI models and practical enterprise applications. Enterprises are now deploying AI agents to free human workers from time-consuming and error-prone tasks, allowing them to focus on high-value work that requires creativity and strategic thinking. NVIDIA AI Blueprints help enterprises build their own AI agents.
NVIDIA has announced the open-source release of the KAI Scheduler, a Kubernetes-native GPU scheduling solution, now available under the Apache 2.0 license. Originally developed within the Run:ai platform, the KAI Scheduler is now available to the community while also continuing to be packaged and delivered as part of the NVIDIA Run:ai platform. The KAI Scheduler is designed to optimize the scheduling of GPU resources and tackle challenges associated with managing AI workloads on GPUs and CPUs. Recommended read:
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@tomshardware.com
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Nvidia has unveiled its next-generation data center GPU, the Blackwell Ultra, at its GTC event in San Jose. Expanding on the Blackwell architecture, the Blackwell Ultra GPU will be integrated into the DGX GB300 and DGX B300 systems. The DGX GB300 system, designed with a rack-scale, liquid-cooled architecture, is powered by the Grace Blackwell Ultra Superchip, combining 36 NVIDIA Grace CPUs and 72 NVIDIA Blackwell Ultra GPUs. Nvidia officially revealed its Blackwell Ultra B300 data center GPU, which packs up to 288GB of HBM3e memory and offers 1.5X the compute potential of the existing B200 solution.
The Blackwell Ultra GPU promises a 70x speedup in AI inference and reasoning compared to the previous Hopper-based generation. This improvement is achieved through hardware and networking advancements in the DGX GB300 system. Blackwell Ultra is designed to meet the demand for test-time scaling inference with a 1.5X increase in the FP4 compute. Nvidia's CEO, Jensen Huang, suggests that the new Blackwell chips render the previous generation obsolete, emphasizing the significant leap forward in AI infrastructure. Recommended read:
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Cierra Choucair@The Quantum Insider
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Nvidia CEO Jensen Huang unveiled the company's latest advancements in AI and quantum computing at GTC 2025, emphasizing a clear roadmap for data center computing, AI reasoning, robotics, and autonomous vehicles. The centerpiece was the Blackwell platform, now in full production, boasting a 40x performance leap over its predecessor, Hopper, crucial for inference workloads. Nvidia is also countering the DeepSeek efficiency challenge, with focus on the Rubin AI chips slated for late 2026.
Nvidia is establishing the NVIDIA Accelerated Quantum Research Center (NVAQC) in Boston to integrate quantum hardware with AI supercomputers. The center will collaborate with industry leaders and top universities to address quantum computing challenges. NVAQC is set to begin operations later this year, supporting the broader quantum ecosystem by accelerating the transition from experimental to practical quantum computing. NVAQC will employ the NVIDIA GB200 NVL72 systems and CUDA-Q platform to power research on quantum simulations, hybrid quantum algorithms, and AI-driven quantum applications. Recommended read:
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Ronen Dar@NVIDIA Technical Blog
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NVIDIA has announced the open-source release of the KAI Scheduler, a Kubernetes-native GPU scheduling solution. Available under the Apache 2.0 license, the KAI Scheduler was originally developed within the Run:ai platform. This initiative aims to foster an active and collaborative community by encouraging contributions, feedback, and innovation in AI infrastructure. The KAI Scheduler will continue to be packaged and delivered as part of the NVIDIA Run:ai platform.
NVIDIA's move to open source the KAI Scheduler addresses challenges in managing AI workloads on GPUs and CPUs, which traditional resource schedulers often fail to meet. The scheduler dynamically manages fluctuating GPU demands, reduces wait times for compute access by combining gang scheduling, GPU sharing, and a hierarchical queuing system, and it helps to connect AI tools and frameworks seamlessly. By maximizing compute utilization through bin-packing, consolidation and spreading workloads across nodes, KAI Scheduler reduces resource fragmentation. Recommended read:
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Cierra Choucair@The Quantum Insider
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NVIDIA is establishing the Accelerated Quantum Research Center (NVAQC) in Boston to integrate quantum hardware with AI supercomputers. The aim of the NVAQC is to enable accelerated quantum supercomputing, addressing quantum computing challenges such as qubit noise and error correction. Commercial and academic partners will work with NVIDIA, with collaborations involving industry leaders like Quantinuum, Quantum Machines, and QuEra, as well as researchers from Harvard's HQI and MIT's EQuS.
NVIDIA's GB200 NVL72 systems and the CUDA-Q platform will power research on quantum simulations, hybrid quantum algorithms, and AI-driven quantum applications. The center will support the broader quantum ecosystem, accelerating the transition from experimental to practical quantum computing. Despite the CEO's recent statement that practical quantum systems are likely still 20 years away, this investment shows confidence in the long-term potential of the technology. Recommended read:
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Ellie Ramirez-Camara@Data Phoenix
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Nvidia's GTC 2025 event showcased the company's latest advancements in AI computing. A key highlight was the introduction of the Blackwell Ultra platform, designed to support the growing demands of AI reasoning, agentic AI, and physical AI applications. This next-generation platform builds upon the Blackwell architecture and includes the GB300 NVL72 rack-scale solution and the HGX B300 NVL16 system.
The Blackwell Ultra platform promises significantly enhanced AI computing power, with the GB300 NVL72 delivering 1.5x more AI performance than its predecessor and increasing revenue opportunities for AI factories by 50x. Major cloud providers and server manufacturers are expected to offer Blackwell Ultra-based products in the second half of 2025. Supporting this hardware is the new NVIDIA Dynamo open-source inference framework, which optimizes reasoning AI services across thousands of GPUs. Recommended read:
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Jesus Rodriguez@TheSequence
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NVIDIA's GTC 2025 showcased the company's advancements in AI hardware and software, solidifying its position as a leader in the AI compute industry. The conference highlighted the synergy between NVIDIA's hardware and software offerings, emphasizing AI's pervasive influence across various sectors. Key announcements included the Blackwell Ultra AI Factory Platform, boasting 72 Blackwell Ultra GPUs and 36 Grace CPUs, designed for demanding AI agent workloads. NVIDIA also previewed future platforms, such as the Rubin Ultra NVL576 slated for late 2027, showcasing their commitment to continuous innovation.
NVIDIA also unveiled the Llama Nemotron family of open-source reasoning models, designed for superior accuracy and speed compared to standard models. These models are already being integrated by major players like Microsoft and SAP into their respective platforms. Furthermore, NVIDIA launched Dynamo, an open-source inference framework aimed at maximizing GPU performance through intelligent scheduling. The event, which attracted an estimated 25,000 attendees, underscored NVIDIA's role as a key enabler of AI advancements, driving innovation from healthcare to autonomous vehicles. Recommended read:
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Ryan Daws@AI News
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NVIDIA has launched Dynamo, an open-source inference software, designed to accelerate and scale reasoning models within AI factories. Dynamo succeeds the NVIDIA Triton Inference Server, representing a new generation of AI inference software specifically engineered to maximize token revenue generation for AI factories deploying reasoning AI models. The software orchestrates and accelerates inference communication across thousands of GPUs, utilizing disaggregated serving.
Dynamo optimizes AI factories by dynamically managing GPU resources in real-time to adapt to request volumes. Dynamo’s intelligent inference optimizations have shown to boost the number of tokens generated by over 30 times per GPU and has demonstrated the ability to double the performance and revenue of AI factories serving Llama models on NVIDIA’s current Hopper platform. Recommended read:
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Sam Khosroshahi@lambdalabs.com
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NVIDIA is pushing the boundaries of artificial intelligence in healthcare and robotics, introducing several groundbreaking advancements. One notable innovation is the DNA LLM, designed to decode the complex genetic information found in DNA, RNA, and proteins. This tool aims to transform genomic research, potentially leading to new understandings and treatments for various diseases.
The company's commitment to AI extends to robotics with the release of Isaac GR00T N1, an open-source platform for humanoid robots. This initiative is expected to accelerate innovation in the field, providing developers with the resources needed to create more advanced and capable robots. Additionally, an NVIDIA research team has developed Hymba, a family of small language models that combine transformer attention with state space models, surpassing the Llama-3.2-3B model in performance while significantly reducing cache size and increasing throughput. Recommended read:
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Michael Nuñez@venturebeat.com
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venturebeat.com
, AIwire
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Nvidia has made significant strides in enhancing robot training and AI capabilities, unveiling innovative solutions at its GTC conference. A key announcement was Cosmos-Transfer1, a groundbreaking AI model designed to generate photorealistic simulations for training robots and autonomous vehicles. This model bridges the gap between virtual training environments and real-world applications by using multimodal inputs to create highly realistic simulations. This adaptive multimodal control system allows developers to weight different visual inputs, such as depth or object boundaries, to improve the realism and utility of the generated environments.
Nvidia also introduced its next-generation GPU superchips, including the second generation of the Grace Blackwell chip and the Vera Rubin, expected in the second half of 2026. The Vera Rubin will feature 288GB of high-bandwidth memory 4 (HBM4) and will be paired with CPUs boasting 88 custom Arm cores. These new chips promise substantial increases in compute capacity, with Rubin delivering a 900x speedup compared to the previous generation Hopper chips. This positions Nvidia to tackle the increasing demands of generative AI workloads, including training massive AI models and running inference workloads. Recommended read:
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Dean Takahashi@AI News | VentureBeat
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NVIDIA, Google DeepMind, and Disney Research are collaborating on Newton, an open-source physics engine designed to advance robot learning, enhance simulation accuracy, and facilitate the development of next-generation robotic characters. Newton is built on NVIDIA’s Warp framework and aims to provide a scalable, high-performance simulation environment optimized for AI-driven humanoid robots. MuJoCo-Warp, a collaboration with Google DeepMind, accelerates robotics workloads by over 70x, while Disney plans to integrate Newton into its robotic character platform for expressive, interactive robots.
The engine's creation is intended to bridge the gap between simulation and real-world robotics. NVIDIA will also supercharge humanoid robot development with the Isaac GR00T N1 foundation model for human-like reasoning. Newton is built on NVIDIA Warp, a CUDA-based acceleration library that enables GPU-powered physics simulations. Newton is also optimized for robot learning frameworks, including MuJoCo Playground and NVIDIA Isaac Lab, making it an essential tool for developers working on generalist humanoid robots. This initiative is part of NVIDIA's broader effort to accelerate physical AI progress. Recommended read:
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Brad Smith@NVIDIA Technical Blog
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Nvidia is making significant strides in the realm of physical AI, positioning itself as a frontrunner in this emerging field. The company's strategy involves creating a full-stack infrastructure tailored for robotics. This comprehensive approach encompasses hardware, software, simulation tools, and pre-trained models, aiming to support various industries in developing advanced robotic solutions. Nvidia's ambition is to provide developers with the necessary tools, like chips, libraries, models, and data pipelines, to build their own solutions.
Nvidia is also pushing the boundaries of data center networking through the integration of silicon photonics. The company unveiled the NVIDIA Quantum and NVIDIA Spectrum switch ICs, which represent advancements in data center networking. This innovation enables accelerated data processing and is vital for supporting the increasing demands of AI workloads, driving further developments in areas such as drug discovery and renewable energy management. Recommended read:
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James McKenna@NVIDIA Newsroom
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NVIDIA Newsroom
, NVIDIA Technical Blog
NVIDIA's Omniverse platform is gaining traction within industrial ecosystems as companies leverage digital twins to train physical AI. The Mega NVIDIA Omniverse Blueprint, now available in preview, empowers industrial enterprises to accelerate the development, testing, and deployment of physical AI. This blueprint provides a reference workflow for combining sensor simulation and synthetic data generation, enabling the simulation of complex human-robot interactions and verification of autonomous systems within industrial digital twins.
At Hannover Messe, leaders from manufacturing, warehousing, and supply chain sectors are showcasing their adoption of the blueprint to simulate robots like Digit from Agility Robotics. They are also demonstrating how industrial AI and digital twins can be used to optimize facility layouts, material flow, and collaboration between humans and robots. NVIDIA ecosystem partners like Delta Electronics, Rockwell Automation, and Siemens are also announcing further integrations with NVIDIA Omniverse and NVIDIA AI technologies at the event, further solidifying Omniverse's role in physical AI development. Recommended read:
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Hassan Shittu@Fello AI
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John Werner
, TheSequence
NVIDIA's GPU Technology Conference (GTC) 2025 concluded in San Jose, California, marking a week filled with significant announcements in AI hardware and software. CEO Jensen Huang highlighted advancements in AI computing, emphasizing the potential of new chips like those in the Grace Blackwell product line, which offers an exaflop of computing power and revolutionary liquid cooling. Huang also laid out plans for future chip iterations, including the Vera Ruben NVL144, signaling NVIDIA's continued commitment to pushing the boundaries of AI capabilities and data center designs.
The conference also highlighted the NVIDIA DNA LLM and the potential of AI in genomic research. NVIDIA is transforming this field by applying artificial intelligence (AI) techniques, including large language models (LLMs), to analyze and interpret biological data. Evo 2, the largest AI model ever created for biology, can generate entire genomic sequences from scratch. John Snow Labs introduced the first commercially available medical reasoning LLM at NVIDIA GTC. The new models are optimized specifically for clinical reasoning, can verbalize their chain of thought, and apply medically recommended planning and decision-making processes. Recommended read:
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@tomshardware.com
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BigDATAwire
, www.tomshardware.com
AMD has announced Gaia, an open-source project enabling the local execution of Large Language Models (LLMs) on any PC. This initiative aims to bring AI processing closer to users by facilitating the running of LLMs directly on Windows machines. Gaia is designed to run various LLM models and offers performance optimizations for PCs equipped with Ryzen AI processors, including the Ryzen AI Max 395+.
Gaia utilizes the open-source Lemonade SDK from ONNX TurnkeyML for LLM inference, allowing models to adapt for tasks such as summarization and complex reasoning. It functions through a Retrieval-Augmented Generation agent, combining an LLM with a knowledge base for interactive AI experiences and accurate responses. Gaia offers several agents including Simple Prompt Completion, Chaty, Clip, and Joker. The new AI chatbot has two installers: a mainstream installer that works on any Windows PC and a "Hybrid" installer optimized for Ryzen AI PCs, using the XDNA NPU and RDNA iGPU. Recommended read:
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Marco Chiappetta@hothardware.com
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hothardware.com
, NVIDIA Technical Blog
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NVIDIA continues to assert its dominance in both AI infrastructure and mobile gaming. The company is blitzing the laptop gaming market with its flagship GeForce RTX 5090 Laptop GPU, hailed as the fastest mobile GPU ever tested. This new GPU, based on the Blackwell architecture, promises to deliver enhanced performance and features for gamers and content creators on the go. The RTX 5090 Laptop GPU incorporates updated shader cores, 4th gen RT cores, and 5th gen Tensor cores, supporting DLSS 4 and a new media engine.
Nvidia is taking major steps to promote its open source enterprise AI infrastructure. NVIDIA has announced the open-source release of the KAI Scheduler, a Kubernetes-native GPU scheduling solution, now available under the Apache 2.0 license. Originally developed within the Run:ai platform, KAI Scheduler is now available to the community while also continuing to be packaged and delivered as part of the NVIDIA Run:ai platform. This initiative underscores NVIDIA’s commitment to advancing both open-source and enterprise AI infrastructure, fostering an active and collaborative community, encouraging contributions, feedback, and innovation. Recommended read:
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Synced@Synced
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lambdalabs.com
, MarkTechPost
NVIDIA is pushing the boundaries of language models and AI training through several innovative approaches. One notable advancement is Hymba, a family of small language models developed by NVIDIA research. Hymba uniquely combines transformer attention mechanisms with state space models, resulting in improved efficiency and performance. This hybrid-head architecture allows the models to harness both the high-resolution recall of attention and the efficient context summarization of SSMs, increasing the model’s flexibility.
An NVIDIA research team proposes Hymba, a family of small language models that blend transformer attention with state space models, which outperforms the Llama-3.2-3B model with a 1.32% higher average accuracy, while reducing cache size by 11.67× and increasing throughput by 3.49×. The integration of learnable meta tokens further enhances Hymba's capabilities, enabling it to act as a compressed representation of world knowledge and improving performance across various tasks. These advancements highlight NVIDIA's commitment to addressing the limitations of traditional transformer models while achieving breakthrough performance with smaller, more efficient language models. Lambda is honored to be selected as anNVIDIAPartner Network (NPN) 2025 Americas partner of the year award winner in the category of Healthcare. Artificial intelligence systems designed for physical settings require more than just perceptual abilities—they must also reason about objects, actions, and consequences in dynamic, real-world environments. Researchers from NVIDIA introduced Cosmos-Reason1, a family of vision-language models developed specifically for reasoning about physical environments. NVIDIA, a global leader in AI and accelerated computing, is transforming this field by applyingartificial intelligence (AI)techniques, includinglarge language models(LLMs), to analyze and interpret biological data. Recommended read:
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staff@insidehpc.com
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Nvidia CEO Jensen Huang has publicly walked back previous comments made in January, where he expressed skepticism regarding the timeline for quantum computers becoming practically useful. Huang apologized for his earlier statements, which caused a drop in stock prices for quantum computing companies. During the recent Nvidia GTC 2025 conference in San Jose, Huang admitted his misjudgment and highlighted ongoing advancements in the field, attributing his initial doubts to his background in traditional computer systems development. He expressed surprise that his comments had such a significant impact on the market, joking about the public listing of quantum computing firms.
SEEQC and Nvidia announced a significant breakthrough at the conference, demonstrating a fully digital quantum-classical interface protocol between a Quantum Processing Unit (QPU) and a Graphics Processing Unit (GPU). This interface is designed to facilitate ultra-low latency and bandwidth-efficient quantum error correction. Furthermore, Nvidia is enhancing its support for quantum research with the CUDA-Q platform, designed to streamline the development of hybrid, accelerated quantum supercomputers. CUDA-Q performance can now be pushed further than ever with v0.10 support for the NVIDIA GB200 NVL72. Recommended read:
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Jesus Rodriguez@TheSequence
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TheSequence
, BigDATAwire
Nvidia's GTC 2025, held in San Jose, California, concluded this week, showcasing the company's advancements in AI hardware and software. The event, drawing an estimated 25,000 attendees, was described as "The Super Bowl of AI," underscoring Nvidia's dominant position in the high-end GPU market essential for training and running AI models. CEO Jensen Huang, dubbed "AI Jesus," unveiled powerful new hardware like the Blackwell Ultra AI Factory Platform and teased future platforms like Rubin Ultra, demonstrating Nvidia's commitment to meeting the growing compute demands of next-generation AI models.
The conference also highlighted Nvidia's progress on the software front, with the launch of the Llama Nemotron family of open-source reasoning models. These models, designed for accuracy and speed, are already being integrated into platforms like Microsoft's Azure AI Foundry and SAP's Joule copilot. SEEQC and NVIDIA announced they have completed an end-to-end fully digital quantum-classical interface protocol demo between a QPU and GPU. This marks a move towards AI agents capable of solving problems independently. Furthermore, SEEQC and Nvidia reported a breakthrough in quantum computing with a fully digital quantum QPU-GPU interface that leverages Single Flux Quantum (SFQ) technology's ultra-fast clock speeds and on-Quantum Processor digitization to eliminate bandwidth bottlenecks, reduce latency and create an optimal digital link to NVIDIA GPUs. Recommended read:
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Jesse Clayton@NVIDIA Blog
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NVIDIA Newsroom
, AIwire
Nvidia is boosting AI development with its RTX PRO Blackwell series GPUs and NIM microservices for RTX, enabling seamless AI integration into creative projects, applications, and games. This unlocks groundbreaking experiences on RTX AI PCs and workstations. These tools provide the power and flexibility needed for various AI-driven workflows such as AI agents, simulation, extended reality, 3D design, and high-end visual effects.
The new lineup includes a range of GPUs such as the NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 5000 Blackwell, and various laptop GPUs. NVIDIA also introduced the RTX PRO 6000 Blackwell Server Edition GPU, designed to accelerate demanding AI and graphics applications across industries. These advancements redefine data centers into AI factories, manufacturing intelligence at scale and accelerating time to value for enterprises. Recommended read:
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Ben Lorica@Gradient Flow
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Gradient Flow
, bdtechtalks.com
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Nvidia's Dynamo is a new open-source framework designed to tackle the complexities of scaling AI inference operations. Dynamo optimizes how large language models operate across multiple GPUs, balancing individual performance with system-wide throughput. Introduced at the GPU Technology Conference, Nvidia CEO Jensen Huang has described it as "the operating system of an AI factory".
This framework includes components designed to function as an "air traffic control system" for AI processing. These key components include libraries like TensorRT-LLM and SGLang, which provide efficient mechanisms for handling token generation, memory management, and batch processing to improve throughput and reduce latency when serving AI models. Nvidia's nGPT combines transformers and state-space models to reduce costs and increase speed while maintaining accuracy. Recommended read:
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Hassan Shittu@Fello AI
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Fello AI
, lambdalabs.com
Nvidia is making significant strides in healthcare and AI infrastructure, particularly through the development of specialized large language models (LLMs). Their DNA LLM exemplifies this, aiming to revolutionize genomic research and drug discovery. This highlights AI's potential to transform medical science by enabling faster analysis and interpretation of biological data.
Lambda has been recognized as NVIDIA's 2025 Healthcare Partner of the Year for accelerating AI innovation in healthcare and biotech. John Snow Labs introduced the first commercially available Medical Reasoning LLM at NVIDIA GTC, optimized for clinical reasoning and capable of verbalizing its thought processes. Nvidia's involvement in this has helped lead the way for these healthcare specific Large Language Models. Recommended read:
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staff@insidehpc.com
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BigDATAwire
Nvidia's GTC 2025 event showcased the company's advancements in AI, particularly highlighting the integration of AI into various industries. CEO Jensen Huang emphasized that every industry is adopting AI and it is becoming critical for future revenue. Nvidia also unveiled an open Physical AI dataset to advance robotics and autonomous vehicle development. The dataset is claimed to be the world’s largest unified and open dataset for physical AI development, enabling the pretraining and post-training of AI models.
Central to Nvidia’s ambitions for Physical AI is its Omniverse platform, a digital development platform connecting spatial computing, 3D design, and physics-based workflows. Originally designed as a simulation and visualization tool, Omniverse has evolved significantly and has now become more of an operating system for Physical AI, allowing users to train autonomous systems before physical deployment. In quantum computing, SEEQC and Nvidia announced they have completed an end-to-end fully digital quantum-classical interface protocol demo between a QPU and GPU. Recommended read:
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Ali Azhar@AIwire
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AIwire
Nvidia is strategically expanding its AI capabilities with recent acquisitions, signaling a push towards full-stack AI control. The company is reportedly in advanced talks to acquire Lepton AI, a startup specializing in renting out Nvidia-powered servers for AI development. This move, along with the acquisition of synthetic data startup Gretel, demonstrates Nvidia's ambition to move beyond hardware and offer comprehensive AI solutions.
Nvidia's acquisition strategy aims to enhance its cloud-based AI offerings and counter competition from cloud providers developing their own AI chips. The company's interest in Lepton AI and the acquisition of Gretel, known for privacy-safe AI training data, are key steps in its strategy to become a full-stack enabler of AI development. These acquisitions are aimed at integrating into the AI development pipeline and providing more complete solutions for AI development. Recommended read:
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