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NVIDIA, a global leader in chip and AI technology, recently launched a groundbreaking new open-source large language model, Llama 3.1 Nemotron Ultra 253B, generating significant excitement within the AI community. Built upon Meta's Llama-3.1-405B, this model boasts innovative optimizations that surpass competitors like Llama 4 Behemoth and Maverick in performance, while demonstrating superior resource efficiency and exceptional multi-tasking capabilities.

On April 8th, 2025, NVIDIA launched Llama 3.1 Nemotron Ultra 253B, an open-source model optimized from Llama-3.1-405B. With 25.3 billion parameters, it surpasses Meta's Llama 4 Behemoth and Maverick, becoming a focal point in the AI field. This model demonstrates superior performance in benchmarks such as GPQA-Diamond, AIME 2024/25, and LiveCodeBench, achieving inference throughput comparable to DeepSeek.

Recently, Nvidia announced the addition of three safety features on its NeMo Guardrails platform, aimed at helping businesses better manage and control AI chatbots. These microservices target common challenges in AI safety and content moderation, offering a range of practical solutions. Among them, the Content Safety service can review content before the AI responds to users, detecting any potential harmful information. This service helps prevent...

Nvidia has recently released three new NIM (Nvidia Infrastructure Microservices) designed as an extension of the NeMo Guardrails open-source toolkit, aimed at providing more refined security control capabilities for enterprise AI agent applications. Each of these new services has distinct features: the first focuses on content safety to prevent harmful or biased content generated by AI; the second ensures that conversations are limited to approved topics; and the third prevents AI agents from circumventing system restrictions or jailbreaking.

NVIDIA recently announced that its open-source BioNeMo™ biomolecular AI framework has gained widespread adoption in the pharmaceutical field, attracting significant attention from pharmaceutical companies, biotech innovators, and AI researchers. The introduction of this framework marks the formal entry of AI-assisted drug development into the supercomputing era. As a specialized AI toolkit for biomolecular research, the BioNeMo framework offers revolutionary breakthroughs for the pharmaceutical industry. Researchers can leverage this framework to significantly enhance the computational scale of AI models, analyze massive datasets more efficiently, and achieve remarkable advancements.

NVIDIA has open-sourced two new large models, Nemotron-4-Minitron-4B and Nemotron-4-Minitron-8B, which utilize structured pruning and knowledge distillation for efficient training. This significantly reduces training requirements, minimizing data and computing power consumption. Compared to traditional methods, the new models reduce training token data by 40 times and save 1.8 times in computing costs. By optimizing Llama-3.18B, structured pruning simplifies the model structure, while knowledge distillation enhances performance.

Three authors have sued Nvidia for using their copyrighted books without permission to train the NeMo AI platform. The lawsuit claims that the authors' works became part of a dataset consisting of approximately 196,640 books. The authors assert that their works were removed after training in NeMo. Nvidia has not issued a formal response to the case, which could set a significant precedent for copyright in the AI field.

The Nvidia NeMo project demonstrates the application of generative AI in semiconductor chip design, training large language models on internal data to enhance productivity. The research highlights the potential of Nvidia NeMo to provide customized AI models in the semiconductor sector, offering a competitive edge. Nvidia's custom large language model, ChipNeMo, has applications that include chatbots, code generators, and analytical tools, providing a comprehensive boost to semiconductor design.