NVIDIA's Most Powerful AI Chip B100 and OpenAI's Most Advanced AI Model GPT-5 Expected to Launch in 2024

NVIDIA Unveils Multimodal LLM Describe Anything: Generating Detailed Descriptions of Specific Regions

The NVIDIA AI team has released a revolutionary multimodal large language model—Describe Anything 3B (DAM-3B)—designed for detailed, region-specific descriptions of images and videos. This model, with its innovative technology and superior performance, has generated significant discussion in the multimodal learning field, marking another milestone in AI development. Below, AIBase outlines the model's core highlights and industry impact. A breakthrough in region-specific descriptions, DAM-3B stands out for its unique ability to...

Nvidia to Build AI Supercomputer in US for the First Time, Creating Domestic Supply Chain

Google Releases Gemma 3 QAT Model: Runable on a Single RTX 3090

Google recently released a new version of its Gemma3 series, exciting many AI enthusiasts. Just a month after its initial launch, Google released a Quantization Aware Training (QAT) optimized version of Gemma3, aiming to significantly reduce memory requirements while maintaining model quality. Specifically, the QAT-optimized Gemma3 27B model reduces VRAM requirements from 54GB to 14.1GB, meaning users can now run it on a single NVIDIA RTX 3090.

Nvidia Plans US-Based AI Chip Manufacturing

Nvidia recently announced plans to establish over one million square feet of manufacturing space in Arizona and Texas for the production and testing of AI chips. This represents a significant move by Nvidia to bring some of its manufacturing back to the United States. Nvidia's Blackwell chip is already in production at TSMC's Arizona facility. Additionally, Nvidia is establishing "supercomputer" manufacturing plants in Texas, partnering with Foxconn in Houston and Wistron in Dallas. In Arizona, Nvidia is collaborating with Amkor and others.

Betting on a Trillion-Dollar AI Future: Nvidia to Build its First AI Supercomputer on US Soil

AI chip giant Nvidia announced it will partner with manufacturing collaborators to design and build its first AI supercomputer on US soil, marking a significant step in its supply chain strategy. Nvidia has commissioned over one million square feet of manufacturing space for production and testing of its latest Blackwell AI chips in Arizona, and for the manufacturing and testing of the AI supercomputer in Texas. Nvidia's ecosystem partners are expected to invest $500 million to support the construction of this AI infrastructure. While the...

SandboxAQ, Quantum AI Startup, Raises $450 Million with Google and Nvidia Investment

Quantum artificial intelligence startup SandboxAQ announced the successful completion of its Series E funding round, raising $450 million. This round attracted investment from industry giants including Google, Nvidia, and BNP Paribas, bringing SandboxAQ's total funding to $950 million. The company stated that the funds will be used to accelerate the development of its large quantum models and foster collaborations across various industries. Image note: Image generated by AI, image licensing provider Midjourney.

Google Distributed Cloud, Gemini, and NVIDIA Advance On-premises AI for Enterprises

NVIDIA Unveils Llama 3.1 Nemotron Ultra 253B, Outperforming Llama 4 Behemoth

NVIDIA Unveils Llama 3.1 Nemotron Ultra 253B: Redefining AI Performance Standards

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.

NVIDIA Unveils Llama 3.1 Nemotron Ultra 253B: A New Benchmark in Performance

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.