#Llama Nemotron

[govindhtech]

NVIDIA Llama Nemotron Ultra Reinvent Open AI Performance

AI today has deep thinking, complex problem-solving, and powerful adaptability for business, finance, customer service, and healthcare. Producing words and graphics is no longer enough.

The latest NVIDIA Llama Nemotron Ultra reasoning model is available. It improves computing efficiency and has the greatest accuracy among open-source intelligence and coding models. Model, weights, and training data for AI workflow automation, research assistants, and coding copilots are from Hugging Face.

NVIDIA Llama Nemotron Ultra codes maths and science well

Llama Nemotron Ultra redefines AI in scientific thinking, computing, and maths. High-impact AI requires depth and adaptability, therefore the model is built for real-world industrial needs including copilots, information assistants, and automated procedures. Post-trained for sophisticated thinking, RAG, human-aligned discourse, and tool usage.

Llama Nemotron Ultra uses synthetic and commercial data and advanced training methods to improve Llama 3.1. For agentic processes, it offers inexpensive, high-performance AI with solid reasoning. NVIDIA has released two outstanding post-training training datasets accessible to help construct reasoning models.

The community may start building cost-effective, high-performing models using these resources. NVIDIA's @KaggleAI Mathematical Olympiad win in competitive reasoning showed its efficacy. After then, Llama Nemotron Ultra received data, technology, and insights. These three requirements are addressed in depth below.

GPQA Diamond standard

Figures 1, 2 and 3 show that the Llama Nemotron Ultra thinking model outperforms other open models in a scientific reasoning benchmark. PhD-level experts prepared the 198 meticulously designed GPQA Diamond benchmark questions in biology, physics, and chemistry.

Graduate-level problems need deep comprehension and multistep reasoning beyond memory and inference. Although PhDs normally attain 65% accuracy on this challenging subgroup, Llama Nemotron Ultra has set a new standard and became the top open model in scientific reasoning with 76% accuracy. Vellum and Artificial Analysis leaderboards illustrate this.

LiveCodeBench test

Figures 4, 5 show that Llama Nemotron Ultra performs well on complex scientific benchmarks and LiveCodeBench, a solid test for real-world coding abilities. LiveCodeBench focusses on general coding tasks such code creation, debugging, self-repair, test outcome prediction, and execution.

Every problem in LiveCodeBench is date-stamped for unbiased, out-of-distribution evaluation. Prioritising problem-solving above code output checks correct generalisation. Both GitHub LiveCodeBench and Artificial Analysis leaderboards show this.

AIME standard

Llama Nemotron Ultra exceeds other open models in the AIME benchmark, which tests mathematical thinking. Watch the LLM standings live.

Open data and tools

One of Llama Nemotron's greatest achievements is its open design. NVIDIA AI published the model and two commercially viable datasets that powered its reasoning. They're top-trending Hugging Face Datasets.

Over 735K Python examples from 28K questions from popular competitive programming platforms make up the OpenCodeReasoning Dataset. This dataset, designed for supervised fine-tuning (SFT), lets enterprise developers incorporate advanced reasoning into their models. OpenCodeReasoning may help organisations create more intelligent and durable code solutions for AI systems.

The Llama-Nemotron-Post-Training Dataset was artificially constructed using open and public models including DeepSeek-R1, Nemotron, Qwen, and Llama. This dataset improves a model's performance on essential reasoning tasks, making it ideal for general reasoning, math, coding, and instruction following. It helps developers build more competent and coherent AI systems by optimising models to understand and respond to complex, multi-step instructions.

Free datasets on Hugging Face from NVIDIA aim to democratise reasoning model training. Startups, research labs, and enterprises may now use the same resources as NVIDIA internal teams, accelerating the adoption of agentic AI that can reason, plan, and act across complicated workflows.

Enterprise-ready speed, precision, and flexibility

Llama Nemotron Ultra, a commercially successful model, may be used for task-oriented assistants, autonomous research agents, customer care chatbots, and coding copilots. Due to its high scientific reasoning and code benchmark performance, it is ideal for real-world applications that need precision, flexibility, and multistep problem resolution.

Llama Nemotron Ultra has the maximum throughput and model correctness in open-reasoning models. Throughput closely correlates with savings. It uses Neural Architecture Search (NAS) to reduce the model's memory footprint and maintain performance in a data centre. This permits more workloads with less GPUs.

A robust post-training pipeline comprised reinforcement learning (RL) and supervised fine-tuning to improve the model's reasoning and non-reasoning skills. The model's “On” and “Off” capabilities allow businesses to utilise reasoning only when needed, reducing overhead for simpler, non-agentic activities.

[ai-news]

NVIDIA has introduced Llama Nemotron Nano VL, a vision-language model (VLM) designed to address document-level understanding tasks with efficiency and precision. Built on the Llama 3.1 architecture and coupled with a lightweight vision encoder, this #AI #ML #Automation

[3acesnews]

NVIDIA's Llama Nemotron Nano VL Sets New Standards in OCR Accuracy

[news786hz]

Nvidia Launches Llama Nemotron LLMs for Agentic AI

Nvidia Launches Llama Nemotron LLMs for Agentic AI

[arbitcoin]

تنضم Nvidia إلى المركز الوطني للحوسبة الشبكية العالية للسرعة لإنشاء ذكاء الذكاء الاصطناعي في تايوان | سلسلة أخبار Abmedia

أكد الرئيس التنفيذي لشركة Nvidia Huang Renxun على الدور الرئيسي الذي تلعبه تايوان في تقنية الذكاء الاصطناعي العالمي في مؤتمر Computex 2025 الذي عقد في نانغانغ ، تايبيه. تساعد شركات التكنولوجيا التايوانية في بناء نظام بيئي للذكاء الاصطناعي في جميع أنحاء العالم. تقوم حكومة تايوان أيضًا ببناء أجهزة الكمبيوتر المحمولة من الذكاء الاصطناعي لنشر البنية التحتية للمنظمة المعذوية. توفر NVIDIA موارد فنية للعمل مع المركز الوطني للحوسبة الشبكات العالي السرعة لمنظمة العفو الدولية في تايوان (AIمنظمة العفو الدولية السيادية) تطوير الحاسبات الفائقة لتسريع الترويج لابتكار تقنية الذكاء الاصطناعي في تايوان. نفيديا تعلن عن Nvidia و Foxconn (مجموعة Foxconn Hon Hai Technology Group) الشراكة ، سيعمل الطرفان معًا لبناء الحاسبات الفائقة لمصنع AI في تايوان لتوفير أحدث البنية التحتية للتكنولوجيات والباحثين والشركات الجديدة بما في ذلك TSMC. HUIDA والمركز الوطني لحوسبة الشبكة العالية السرعة إنشاء منظمة العفو الدولية يمثل المركز الوطني للحوسبة الوطنية في تايوان على المدى القصير للمركز الوطني التايواني للحوسبة عالية الأداء. NCHCبمساعدة من الحاسوب الخارق الجديد ، سيكون أداء الذكاء الاصطناعي في المركز أعلى بأكثر من 8 مرات من نظام Taiwania 2 الذي تم إطلاقه مسبقًا. يستخدم مشروع NCHC's AI SuperComputer نظام NVIDIA HGX H 200 مع أكثر من 1700 وحدات معالجة الرسومات ، واثنين من NVIDIA GB 200 NVL 72 ونظام NVIDIA HGX B 300 الذي تم تصميمه على منصة NVIDIA Blackwell Ultra. من المقرر أن يتم إطلاق هذه الميزة في وقت لاحق من هذا العام من خلال رابط شبكات Nvidia Quantum Infiniband. تخطط NCHC أيضًا لنشر مجموعة من أجهزة الكمبيوتر العملاقة الشخصية لـ NVIDIA DGX Park و NVIDIA HGX System في السحابة. سيتمكن الباحثون في المؤسسات الأكاديمية التايوانية والوكالات الحكومية والشركات الصغيرة من التقدم لاستخدام النظام الجديد لتسريع خطط الابتكار. قال تشانغ تشوليانغ ، مدير مركز الحوسبة الوطني للسرعة العالية في تايوان ، إن الحواسيب الفائقة الجدد ستعزز مجالات الذكاء الاصطناعى السيادي ، والحوسبة الكمومية والحوسبة العلمية المتقدمة ، وتعزيز الحكم الذاتي التكنولوجي في تايوان. تطوير نموذج لغة الذكاء الاصطناعي التايويان سيدعم الحاسوب الخارق الجديد مشروع AI RAP من تايوان ، وهو منصة تطوير تطبيقات AI. يوفر Taiwan AI Rap نموذج لغة مخصصة يمثل LLM مخصصًا حصريًا للاختلافات الطفيفة الثقافية واللغوية المحلية. تشمل النماذج اللغوية التي توفرها المنصة نموذج محرك تايوان للذكاء الاصطناعي (TAIDE) ، وهو برنامج عام مصمم للتعامل مع اللغة الطبيعية وبناء نماذج اللغة الكبيرة في تايوان (LLMS) لعملاء الذكاء الاصطناعي والترجمة. يوفر البرنامج للشركاء النصوص والصور والمواد الصوتية والفيديو بما في ذلك الحكومات المحلية والمنظمات الإخبارية ووزارة التعليم ووزارة الثقافة والإدارات العامة الأخرى. لدعم تطوير تطبيقات الذكاء الاصطناعى السيادي ، يوفر Taide حاليًا للمطورين سلسلة من طرازات Llama 3.1- Taide Basic ، ويستخدم الفريق نموذج Nvidia Nemotron لإنشاء خدمات ذات سيادة إضافية. يبدأ روبوت الذكاء الاصطناعي التايواني ويقصر وقت تطوير المواد التدريبية استخدم أستاذ جامعي في تاينان نموذج Taide لقيادة روبوت الذكاء الاصطناعي الذي يمكنه التحدث إلى طلاب المدارس الابتدائية والثانوية في التايوانيين والإنجليزية. حتى الآن ، استخدمها أكثر من 2000 طالب ومدرسين وأولياء الأمور. استخدم أستاذ آخر هذا النموذج لإنشاء الكتب المدرسية ، والتي تقصر وقت المعلمين لإعداد الدورات. سيناريوهات تطبيق الرعاية الصحية منظمة العفو الدولية في مجال الرعاية الصحية ، استخدم فريق أبحاث في تايوان نموذج Taide لتطوير chatbot منظمة العفو الدولية مع إمكانات توليد الفهرس المحسّنة التي يمكن أن تساعد مسؤولي السجلات الطبية في توفير معلومات طبية دقيقة ودقيقة للمرضى الذين يعانون من أمراض كبيرة. يقوم مركز مكتب مكافحة الأمراض في تايوان للوقاية من الوباء بتدريب النموذج لإنشاء ملخص الأخبار لدعم تتبع المرض ومنعه. البحث عن منصة Nvidia Earth-2 التي تسرع في تطوير علوم المناخ فيما يتعلق بأبحاث المناخ ، تدعم NCHC الباحثين لاستخدام منصة NVIDIA EARTER-2 لتعزيز البحث والتطوير في علوم الغلاف الجوي. استخدم الباحثون نموذج Corrdiff AI الخاص بـ Earth-2 لتحسين دقة نماذج حل الطقس ، باستخدام نموذج Graphcast من DeepMind في Nvidia Physicsnemo للتنبؤ بالطقس العالمي.

يستخدم NCHC أيضًا NVIDIA NIM Microservices for Fourcastnet ، وهو نموذج NVIDIA يتنبأ بالديناميات في الغلاف الجوي العالمي للطقس والمناخ. باستخدام NVIDIA وحدات معالجة الرسومات لتسريع المحاكاة العددية لنماذج توقعات الطق��. بمساعدة الحاسوب الخارق الجديد ، سيتمكن الباحثون من إجراء عمليات محاكاة أكثر تعقيدًا وتسريع تدريب الذكاء الاصطناعي والتفكير. NVIDIA CUDA-Q و Cuquantum Drive Research Quantum استخدم باحثو NCHC منصة NVIDIA CUDA-Q و NVIDIA CUQUANTUM لتطوير أبحاث الكمبيوتر الكم وتطبيقها في التعلم الآلي الكمومي والكيمياء والتمويل والتشفير وغيرها من المجالات. يستخدم البحث المولدات الجزيئية الكمومية التي طورتها الدوائر الكمومية ومنصة CUDA-Q ، وهي أداة لإنتاج جزيئات كيميائية فعالة. قاموا أيضًا بإنشاء Cutn-QSVM ، وهي أداة مفتوحة المصدر مبنية على cuquantum يمكنها تسريع عمليات محاكاة الدائرة الكمومية على نطاق واسع. تمكن الأداة للباحثين من حل مشاكل أكثر تعقيدًا ، وتوفير قابلية التوسع الخطية ودعم أنظمة الحوسبة الكمومية الهجينة للمساعدة في تسريع تطوير خوارزميات الكم واسعة النطاق. استخدم باحثو NCHC مؤخرًا Cutn-QSVM لمحاكاة خوارزميات التعلم الآلي الكم مع 784 Qubits. يخطط المعهد أيضًا لبناء نظام حوسبة مختلطة للتسارع من خلال دمج نظام NVIDIA DGX الكمي. تحذير المخاطراستثمارات العملة المشفرة محفوفة بالمخاطر للغاية ، وقد تتقلب أسعارها بشكل كبير وقد تفقد كل مديرك. يرجى تقييم المخاطر بحذر.

[enzocalamo]

Nvidia´s New Llama-3.1 Nemotron Just Crushed DeepSeek at HALF the Size!

loadYouTubePlayer('yt_video_dS8QRqErCeM_kg@jwXEOvjpMrb6H');

[goldislops]

Nvidia releases open reasoning models with shared training data

Nvidia unveiled a new family of open weight reasoning models called Llama Nemotron, sharing not only the models but also 30 million training samples and detailed training methods. The three models - ranging from 8 billion to 253 billion parameters - feature toggleable reasoning capabilities, distilling Meta’s open Llama models but adding DeepSeek-like reinforcement learning. This comprehensive release, which includes model weights, post-training data, and technical documentation, enables AI developers to better understand, modify, and build upon Nvidia’s work to create more capable AI systems.

[jcmarchi]

The Sequence Radar #516: NVIDIA’s AI Hardware and Software Synergies are Getting Scary Good

New Post has been published on https://thedigitalinsider.com/the-sequence-radar-516-nvidias-ai-hardware-and-software-synergies-are-getting-scary-good/

The Sequence Radar #516: NVIDIA’s AI Hardware and Software Synergies are Getting Scary Good

The announcements at GTC showcased covered both AI chips and models.

Created Using Midjourney

Next Week in The Sequence:

We do a summary of our series about RAG. The opinion edition discusses whether NVIDIA is the best VC in AI. The engineering installement explores a new AI framework. The research edition explores the amazing Search-R1 model.

You can subscribe to The Sequence below:

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📝 Editorial: NVIDIA’s AI Hardware and Software Synergies are Getting Scary Good

NVIDIA’s GTC never disappoints. This year’s announcements covered everything from powerhouse GPUs to sleek open-source software, forming a two-pronged strategy that’s all about speed, scale, and smarter AI. With hardware like Blackwell Ultra and Rubin, and tools like Llama Nemotron and Dynamo, NVIDIA is rewriting what’s possible for AI development.

Let’s start with the hardware. The Blackwell Ultra AI Factory Platform is NVIDIA’s latest rack-scale beast, packing 72 Blackwell Ultra GPUs and 36 Grace CPUs. It’s 1.5x faster than the previous gen and tailor-made for agentic AI workloads—think AI agents doing real reasoning, not just autocomplete.

Then there’s the long game. Jensen Huang introduced the upcoming Rubin Ultra NVL576 platform, coming in late 2027, which will link up 576 Rubin GPUs using HBM4 memory and the next-gen NVLink interconnect. Before that, in late 2026, we’ll see the Vera Rubin NVL144 platform, with 144 Rubin GPUs and Vera CPUs hitting 3.6 exaflops of FP4 inference—over 3x faster than Blackwell Ultra. NVIDIA’s clearly gearing up for the huge compute demands of next-gen reasoning models like DeepSeek-R1.

On the software side, NVIDIA launched the Llama Nemotron family—open-source reasoning models designed to be way more accurate (20% better) and way faster (5x speed boost) than standard Llama models. Whether you’re building math solvers, code generators, or AI copilots, Nemotron comes in Nano, Super, and Ultra versions to fit different needs. Big names are already onboard. Microsoft’s integrating these models into Azure AI Foundry, and SAP’s adding them to its Joule copilot. These aren’t just nice-to-have tools—they’re key to building a workforce of AI agents that can actually solve problems on their own.

Enter Dynamo, NVIDIA’s new open-source inference framework. It’s all about squeezing maximum performance from your GPUs. With smart scheduling and separate prefill/decode stages, Dynamo helps Blackwell hardware handle up to 30x more requests, all while cutting latency and costs.

This is especially important for today’s large-scale reasoning models, which chew through tons of tokens per query. Dynamo makes sure all that GPU horsepower isn’t going to waste. While Blackwell is today’s star, the Rubin architecture is next in line. Launching late 2026, the Vera Rubin GPU and its 88-core Vera CPU are set to deliver 50 petaflops of inference—2.5x Blackwell’s output. Rubin Ultra scales that to 576 GPUs per rack.

Looking even further ahead, NVIDIA teased the Feynman architecture (arriving in 2028), which will take things up another notch with photonics-enhanced designs. With a new GPU family dropping every two years, NVIDIA’s not just moving fast—it’s setting the pace.

The real story here is synergy. Blackwell and Rubin bring the power. Nemotron and Dynamo help you use it smartly. This combo is exactly what enterprises need as they move toward AI factories—data centers built from the ground up for AI-driven workflows. GTC 2025 wasn’t just a product showcase—it was a blueprint for the next decade of AI. With open models like Nemotron, deployment tools like Dynamo, and next-gen platforms like Rubin and Feynman, NVIDIA’s making it easier than ever to build smart, scalable AI. The future of computing isn’t just fast—it’s intelligent. And NVIDIA’s making sure everyone—from startups to hyperscalers—has the tools to keep up.

🔎 AI Research

Synthetic Data and Differential Privacy

In the paper“Private prediction for large-scale synthetic text generation“ researchers from Google present an approach for generating differentially private synthetic text using large language models via private prediction. Their method achieves the generation of thousands of high-quality synthetic data points, a significant increase compared to previous work in this paradigm, through improvements in privacy analysis, private selection mechanisms, and a novel use of public predictions.

KBLAM

In the paper “KBLAM: KNOWLEDGE BASE AUGMENTED LANGUAGE MODEL” Microsoft Research propose KBLAM, a new method for augmenting large language models with external knowledge from a knowledge base. KBLAM transforms knowledge triples into continuous key-value vector pairs and integrates them into LLMs using a specialized rectangular attention mechanism, differing from RAG by not requiring a separate retrieval module and offering efficient scaling with the knowledge base size.

Search-R1

In the paper “Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning” researchers from the University of Illinois at Urbana-Champaign introduce SEARCH-R1, a novel reinforcement learning framework that enables large language models to interleave self-reasoning with real-time search engine interactions. This framework optimizes LLM rollouts with multi-turn search, utilizing retrieved token masking for stable RL training and a simple outcome-based reward function, demonstrating significant performance improvements on various question-answering datasets.

Cosmos-Reason1

In the paper“Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning” researchers from NVIDIA present Cosmos-Reason1, a family of multimodal large language models specialized in understanding and reasoning about the physical world. The development involved defining ontologies for physical common sense and embodied reasoning, creating corresponding benchmarks, and training models through vision pre-training, supervised fine-tuning, and reinforcement learning to enhance their capabilities in intuitive physics and embodied tasks.

Expert Race

This paper,“Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts”, presents additional results on the ImageNet 256×256 dataset by researchers who trained a Mixture of Experts (MoE) model called Expert Race, building upon the DiT architecture. The results show that their MoE model achieves better performance and faster convergence compared to a vanilla DiT model with a similar number of activated parameters, using a larger batch size and a specific training protocol.

RL in Small LLMs

In the paper “Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn’t” AI researchers investigate the use of reinforcement learning to improve reasoning in a small (1.5 billion parameter) language model under strict computational constraints. By adapting the GRPO algorithm and using a curated mathematical reasoning dataset, they demonstrated significant reasoning gains on benchmarks with minimal data and cost, highlighting the potential of RL for enhancing small LLMs in resource-limited environments.

📶AI Eval of the Weeek

(Courtesy of LayerLens )

Mistral Small 3.1 came out this week with some impressive results. The model seems very strong in programming benchmarks like Human Eval.

Mistral Small 3.1 also outperforms similar size models like Gemma 3.

🤖 AI Tech Releases

Claude Search

Anthropic added search capabilities to Claude.

Mistral Small 3.1

Mistral launched Small 3.1, a multimodal small model with impressive performance.

Model Optimization

Pruna AI open sourced its famout AI optimization framework.

📡AI Radar

NVIDIA acquired synthetic data platform Gretel AI.

Perplexity is raising a new round at $18 billion valuation.

SoftBank announced the acquisition of semiconductor platform Ampere Computing.

Data analytic company Dataminr raised $85 million in new funding.

AI security platform Orion Security emerged from stealth mode with $6 million in funding.

Roblox launched Roblox Cube, a new gen AI system for 3D and 4D assets.

Halliday blockchain-agentic platform raised $20 million in new funding.

ClearGrid raised $10 million to automated debt collection with AI.

Tera AI raised $7.8 million for its robotics navigation platform.

AI presentation platform Present raised $20 million in new funding.

[pulipuli]

看看網頁版全文 ⇨ 只用CPU跑「小型」語言模型可行嗎？ / Is Running "Small" Language Models on CPUs Only Feasible? https://blog.pulipuli.info/2025/02/is-running-small-language-models-on-cpus-only-feasible.html 很多人都說跑大型語言模型需要很高級的GPU，其實相對於門檻較高的大型語言模型，小型語言模型也一直在如火如荼地發展。 最近我嘗試用12核CPU跟32GB的RAM來跑Gemma2:2B，意外地很順利呢。 Many people say that running large language models requires high-end GPUs. However, relative to the higher barrier to entry of large language models, small language models have also been developing rapidly. Recently, I experimented with running Gemma2:2B using a 12-core CPU and 32GB of RAM, and it went surprisingly smoothly.。 ---- # 小型模型語言 / Small Language Models。 https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct。 一般我們熟知的大型語言模型擁有快速且聰明的推理能力，但大型的模型也意味著我們需要較更好的硬體才能使它順利運作。 舉例來說，在Chatbot Arena LLM Leaderboard排行較為前面、並且有註明參數量的模型中，第16名的nvidia/Llama-3.1-Nemotron-70B-Instruct的參數量來到了 705 億個參數，而用它推理所需要的硬體設備，建議是4張40GB、或是2張80GB的NVIDIA顯示卡，以及150GB以上的硬碟空間。 這種等級的硬體設備完全是資料中心層級才能駕馭，對我們這種市井小民來說都是遙不可及。 https://www.ibm.com/think/topics/small-language-models。 為了使大型語言模型能夠用於更多應用場景，研究者利用剪裁(pruning)、量化(quantization)、低階因式分解(low-rank factorization)、知識蒸餾(knowledge distillation)等方法對模型進行壓縮。 ---- 繼續閱讀 ⇨ 只用CPU跑「小型」語言模型可行嗎？ / Is Running "Small" Language Models on CPUs Only Feasible? https://blog.pulipuli.info/2025/02/is-running-small-language-models-on-cpus-only-feasible.html

[newtras]

Nvidia's AI agent play is here with new models, orchestration blueprints

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. learn more The industry push to agent AI continues, with Nvidia announces several new services and modules to facilitate the creation and use of AI agents. Today, Nvidia launched Nemotron, a family of modules based on it Meta's Llama and received training on the company's methods…

[satrthere]

Nvidia's AI agent play is here with new models, orchestration blueprints

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. learn more The industry push to agent AI continues, with Nvidia announces several new services and modules to facilitate the creation and use of AI agents. Today, Nvidia launched Nemotron, a family of modules based on it Meta's Llama and received training on the company's methods…

[govindhtech]

Cadence Debut Millennium M2000 Supercomputer With NVIDIA

Cadence unveils an NVIDIA-accelerated supercomputer at CadenceLIVE, transforming engineering simulation and design.

Silicon Valley CadenceLIVE

At its annual CadenceLIVE Silicon Valley event today, Cadence Design Systems announced a huge development in AI-driven engineering design and scientific simulation through close integration with NVIDIA accelerated computing hardware and software. NVIDIA's newest technology powers the Millennium M2000 Supercomputer, which offers unmatched performance for digital twin simulations, drug discovery, and semiconductor design.

NVIDIA Millennium M2000 Supercomputer

Millennium M2000 Supercomputer surpasses CPU-based predecessor. RTX PRO 6000 Blackwell Server Edition GPUs and NVIDIA HGX B200 systems are included. Hardware and optimised software like NVIDIA CUDA-X libraries power the system. This mix of cutting-edge hardware and customised software is touted to produce up to 80x better performance for critical system design, EDA, and biological research tasks than the previous generation. Engineers and academics may now run complex, comprehensive simulations thanks to this speed boost.

This enhanced processing capability should lead to advances in several areas. The Millennium Supercomputer accelerates molecular design, data centre design, circuit modelling, and CFD. that more accurate insights enable faster pharmaceutical, system, and semiconductor development.

It may affect the development of pharmaceuticals, data centres, semiconductors, and autonomous robots. The sources also include Cadence's platform integrations, such as NVIDIA Llama Nemotron reasoning models in the JedAI Platform and NVIDIA BioNeMo NIM microservices in Orion.

CadenceLIVE featured the CEO and founder of NVIDIA and Cadence's president discussing the relationship behind this new supercomputer. Devgan says this discovery has been “years in the making,” requiring Cadence to update its software and NVIDIA to upgrade hardware and systems to take use of the new capabilities. Leaders emphasised cooperative initiatives on digital twins, agentic AI, and AI factories. AI will permeate everything humans do, and “every company will be run better because of AI, or they’ll build better products because of AI.”

NVIDIA aims to buy 10 Millennium Supercomputers based on the GB200 NVL72 architecture, emphasising this relationship. This significant acquisition aims to speed up NVIDIA's chip design processes. Huang said NVIDIA has started developing its data centre infrastructure to prepare for this purchase, calling it a “big deal”.

The sources provide examples of this sophisticated technology's use. NVIDIA engineers utilised Cadence Palladium emulation and Protium prototype systems for chip bring-up and design verification during Blackwell development. However, Cadence modelled aeroplane takeoff and landing fluid dynamics using the Cadence Fidelity CFD Platform and NVIDIA Grace Blackwell-accelerated systems.

The NVIDIA GB200 Grace Blackwell Superchips and Cadence platform completed a “highly complex” simulation in less than 24 hours that would have taken days on a huge CPU cluster with hundreds of thousands of cores. Cadence used NVIDIA Omniverse APIs to display these complicated fluid dynamics.

Integration covers AI infrastructure design and optimisation as well as physical simulations. Cadence uses the NVIDIA Omniverse Blueprint and Cadence Reality Digital Twin Platform for AI industrial digital twins. This connection lets engineering teams employ physically based models to optimise AI factory components like energy, cooling, and networking before construction. This functionality makes next-generation AI factories future-proof and speeds up setup decisions.

Live Silicon Valley 2025

CadenceLIVE Silicon Valley 2025 featured the Millennium M2000 Supercomputer and the wide relationship. At the Santa Clara Convention Centre on May 7, 2025, Cadence users may network with engineers, business leaders, and experts in electrical design and intelligent systems.

Cadence describes LIVE Silicon Valley 2025 as a day of education, networking, and cutting-edge technology. Participants can improve by understanding best practices and practical solutions. Keynote speeches from industry pioneers are a highlight of the event. The Designer Expo showcases cutting-edge concepts and connects attendees with Cadence experts and innovators. It brings brilliant people together for a day of inspiration and creativity.

The Cadence-NVIDIA collaboration, highlighted by the Millennium M2000 Supercomputer and its presentation at CadenceLIVE, seeks to integrate AI and accelerated computing into engineering design and scientific discovery by drastically reducing time and cost and enabling previously unattainable simulation complexity and detail.

[ai-news]

NVIDIA has released Llama Nemotron Nano 4B, an open-source reasoning model designed to deliver strong performance and efficiency across scientific tasks, programming, symbolic math, function calling, and instruction following—while being compact eno #AI #ML #Automation

[3acesnews]

NVIDIA Unveils Llama-Nemotron Dataset to Enhance AI Model Training

[hadnewscom]

New, Groundbreaking AI Model Outperforms GPT-40 with Surprising Success – Video – GretAi News-Nvidia’s new AI model, Llama-3.1-Nemotron-70B-Instruct, is outperforming GPT-4o with impressive benchmark results. It quietly launched on Hugging Face, surpassing OpenAI’s and Anthropic’s models in multiple key performance tests. The model’s strong scores and

[nextaitool]

Nvidia’s New AI Steps Into the Spotlight

Nvidia’s new AI model bringing smarter tech and real advantages for everyday businesses.

Nvidia just shook up the AI world with a surprise release. Meet Llama-3.1-Nemotron-70B-Instruct. It’s an AI model that's got everyone buzzing. Why? Because it beats big names like OpenAI's GPT-4.

Nvidia's Unexpected Hit

Nvidia launched this model on Hugging Face on Tuesday. Thing is, they skipped the big fanfare. Yet, it still caught everyone's eye. Why? Simple. It blew existing benchmarks out of the water. Here's the scoop: - Arena Hard benchmark: Scored 85.0 - AlpacaEval 2 LC: Hit 57.6 - GPT-4-Turbo MT-Bench: Achieved 8.98

What's the takeaway? Nvidia's new model is now the leader in language tricks and AI smarts.

Nvidia: From Chips to Code

Nvidia's played a big role in making GPUs—the backbone of AI tech. Now, they’re diving deep into AI software. Before, they were all about the hardware. Now, with Llama-3.1-Nemotron-70B-Instruct, they’ve proven they're serious about AI software too. They took Meta’s Llama 3.1 and made it even better using cool methods like RLHF, which helps the model think more like us humans.

Why It Matters for Businesses

Nvidia wants their AI to align with what users want. This means happier customers. For businesses, that’s a huge plus. It means fewer mistakes and better customer service.

Thinking about trying AI? Check out Nvidia’s model because: - It’s free to test on their build.nvidia.com. - It works with OpenAI APIs.

That means any business can tap into advanced AI tools without breaking the bank.

In short, Nvidia’s new model is worth a look if you’re exploring AI. Just keep its strengths and limits in mind, and you’ll be ahead in the AI game.

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