Peter Barrett’s Post

NVIDIA continues to push the boundaries in generative AI, highlighted by their latest achievements in the MLPerf benchmarks. NVIDIA TensorRT-LLM software has supercharged the inference process for large language models on NVIDIA Hopper Architecture GPUs, resulting in a 3x performance boost compared to six months ago. Leading organisations are already leveraging TensorRT-LLM to fine-tune their models 🚀 Learn more: https://bit.ly/3JelyFh #NVIDIA #ArtificialIntelligence #AI

🚀 Introducing Llama-3.1-Nemotron-51B 🚀 Exciting times in the world of AI! NVIDIA has unveiled the Llama-3.1-Nemotron-51B, a groundbreaking language model that sets a new benchmark for accuracy and efficiency. Derived from Meta’s Llama-3.1-70B, this model leverages a novel Neural Architecture Search (NAS) approach, resulting in a highly accurate and efficient model that fits on a single NVIDIA H100 GPU at high workloads. 🌟 Key Highlights: Unmatched Performance: Achieves 2.2x faster inference compared to the reference model while maintaining nearly the same accuracy. Efficiency at its Best: Reduced memory footprint and bandwidth, enabling 4x larger workloads on a single GPU. Innovative Approach: Combines NAS and knowledge distillation to deliver superior throughput and workload efficiency. This advancement not only pushes the boundaries of what’s possible in AI but also makes high-performance models more accessible and affordable. A huge leap forward for the AI community! Read more https://lnkd.in/gGZiQk3t #AI #MachineLearning #NVIDIA #Innovation #TechNews

Wow DDN Storage packs 12 petabytes of highest performance flash in this NVIDIA DGX SuperPOD for generative AI at scale.

This is wild, give it a read. #Gamer me is still trying to wrap my head around NVIDIA working directly with 3GPP. From the article, "NVIDIA contributed to the completion of the 3GPP study on AI/ML for the 5G NR air interface in Release 18. NVIDIA is now contributing to the 3GPP Release 19 work item on AI/ML for 5G NR air interface, introducing specification support for AI/ML usage in 5G-Advanced toward 6G." CUDA is coming into the picture as RAN data is placed in a datalake, to achieve AI-Accelerated RAN.

The platform consists of a digital twin to enable users to simulate accurate radio environments for #5G and eventually #6G systems; a software-defined, full-RAN stack that allows researchers to customize, program and test #6G networks in real time; and a neural radio framework that uses Nvidia GPUs to train AI and machine learning models at scale

As AI algorithms drive the development of 5G and future 6G networks, the need for AI-native tools is on the rise. Generating synthetic data, training algorithms, and thorough testing are crucial steps before deployment. Learn more about how #NVIDIA is shaping the future of wireless networks: https://lnkd.in/gF_KVusM Ryuji Wakikawa Jinsung Choi Charlie Zhang Mathias Riback Ron Marquardt Harish Viswanathan Robert Soni Srinivasa (Srini) Kalapala Giampaolo Tardioli Ravi Sinha Lopamudra K. Chris Dick Kuntal Chowdhury Tommaso Melodia Pertti Lukander

@databricks.com by Nikhil Sardana, Julian Quevedo and Daya Khudia Quote article "Quantization produces a smaller and faster model. Reducing the size of the model allows us to use less GPU memory and/or increase the maximum batch size. A smaller model also reduces the bandwidth required to move weights from memory. " #AI #machinelearningalgorithms #machinelearning #artificialintelligence #neuralnetwork #nvidia #databricks #oracleai #azureai #llm https://lnkd.in/eDajV7zf

Nvidia's flagship H100 GPU, introduced in 2022, quickly became the top choice for AI data center operators through 2023. The H100 capitalizes on GPUs' inherent advantage in parallel processing, allowing them to handle multiple tasks simultaneously with impressive throughput. These GPUs are equipped with significant amounts of onboard memory, which enhances their capability to train large AI models and perform AI inference tasks efficiently. The rapid adoption of AI applications is expected to spark a productivity surge globally, with the potential to generate up to $200 trillion in economic activity by 2030, according to projections by Cathie Wood's ARK Investment. Major tech companies, including Microsoft and Amazon, are investing heavily in AI GPUs, filling their data centers with them to rent out processing power to developers, who gain access to high-performance infrastructure without having to invest billions in hardware. This arrangement benefits everyone involved—tech companies and Nvidia profit from demand, while developers gain access to scalable AI resources at a fraction of the cost of building their own infrastructure. While Nvidia's H100 and its newer successor, the H200, remain highly sought after, the company's latest Blackwell architecture represents an unprecedented leap forward in performance. Blackwell-powered GB200 GPUs can conduct AI inference at speeds up to 30 times faster than the H100, enabling developers to tackle even more demanding AI workloads. Nvidia CEO Jensen Huang has indicated that individual GB200 GPUs will be priced similarly to the initial price range of the H100, around $30,000 to $40,000, highlighting Nvidia's commitment to pushing the boundaries of AI processing power while maintaining pricing at established levels for top-tier GPUs. https://lnkd.in/en_NB5ry

NVIDIA's Llama-3.1-Nemotron-51B: Redefining Efficiency and Accuracy - NVIDIA's Llama-3.1-Nemotron-51B outperforms traditional models with a 2.2x increase in inference speed, fitting large workloads on a single GPU. - Utilizes Neural Architecture Search (NAS) and advanced block-distillation techniques, cutting costs while maintaining top-tier accuracy. - Optimized for diverse deployment scenarios, making high-performance language models more accessible and affordable for businesses. Read more: https://lnkd.in/g8DQKY_H #AI #LLMs #NVIDIA #Efficiency

Redefining the Future of AI with OpenAI and Nvidia The future of artificial intelligence is here, and it's being powered by unprecedented collaboration and innovation. OpenAI has just received the world’s first Nvidia DGX H200 AI supercomputer, personally delivered by Nvidia CEO Jensen Huang. This cutting-edge system is a game-changer, designed to tackle the challenges of training increasingly complex AI models—like OpenAI’s upcoming GPT-5, which aims to achieve Artificial General Intelligence (AGI). Built on Nvidia’s Hopper architecture, the DGX H200 delivers: - 1.4x more memory bandwidth - 1.8x greater memory capacity - HBM3e memory for efficient large dataset processing These advancements don’t just mean faster computations—they enable entirely new possibilities in generative AI and high-performance computing. As AI models grow in complexity, this partnership exemplifies the importance of integrating cutting-edge hardware and sophisticated software to drive innovation. The DGX H200 is paving the way for groundbreaking advancements in AI research and deployment, setting a new standard for what’s possible in the journey toward AGI. What role do you think hardware innovations will play in shaping the future of AI? #AI #Innovation #OpenAI #Nvidia #AGI #GenerativeAI #FutureOfTech https://lnkd.in/gnpTECQz