Nvidia launched the new version of its frontier models, Nemotron 3, by leaning in on a model architecture that the world’s most valuable company said offers more accuracy and reliability for agents. Nemotron 3 will be available in three sizes: Nemotron 3 Nano with 30B parameters, mainly for targeted, highly efficient tasks; Nemotron 3 Super, which is a 100B parameter model for multi-agent applications and with high-accuracy reasoning and Nemotron 3 Ultra, with its large reasoning engine and around 500B parameters for more complex applications. To build the Nemotron 3 models, Nvidia said it leaned into a hybrid mixture-of-experts (MoE) architecture to improve scalability and efficiency. By using this architecture, Nvidia said in a press release that its new models also offer enterprises [...]
The generative AI era began for most people with the launch of OpenAI's ChatGPT in late 2022, but the underlying technology — the "Transformer" neural network architecture that allows [...]
IBM today announced the release of Granite 4.0, the newest generation of its homemade family of open source large language models (LLMs) designed to balance high performance with lower memory and cost [...]
Jensen Huang walked onto the GTC stage Monday wearing his trademark leather jacket and carrying, as it turned out, the blueprints for a new kind of monopoly.The Nvidia CEO unveiled the Agent Toolkit, [...]
Nvidia on Monday took the wraps off Vera Rubin, a sweeping new computing platform built from seven chips now in full production — and backed by an extraordinary lineup of customers that includes Ant [...]
Multi-agent systems, designed to handle long-horizon tasks like software engineering or cybersecurity triaging, can generate up to 15 times the token volume of standard chats — threatening their cos [...]
When the transformer architecture was introduced in 2017 in the now seminal Google paper "Attention Is All You Need," it became an instant cornerstone of modern artificial intelligence. Ever [...]
For the last two years, the prevailing logic in generative AI has been one of brute force: if you want better reasoning, you need a bigger model. While "small" models (under 10 billion param [...]
Nvidia CEO Jensen Huang said last year that we are now entering the age of physical AI. While the company continues to offer LLMs for software use cases, Nvidia is increasingly positioning itself as a [...]
The prevailing assumption in AI development has been straightforward: larger models trained on more data produce better results. Nvidia's latest release directly challenges that size assumption â [...]