Nvidia CEO Jensen Huang is making a calculated bet that might surprise the tech world - embracing open-source AI not as a gift to developers, but as a survival strategy. According to a new CNBC column, the GPU giant's shift toward openness signals something deeper: the company's traditional hardware moat is under siege, and Huang knows it. The move marks a pivotal moment for the $2 trillion chip maker as competitors circle and customers grow restless.
NVIDIA released a series of open models to advance science, including Nemotron for agentic AI, BioNemo for biomedical applications & Cosmos for physical AI
For several decades now, the most innovative software has always emerged from the world of open source software. It’s no different with machine learning and large language models. If anything, the open source ecosystem has grown richer and more complex, because now there are open source models to complement the open source code.
LosslessCut aims to be the ultimate cross platform FFmpeg GUI for extremely fast and lossless operations on video, audio, subtitle and other related media files. The main feature is lossless trimming and cutting of video and audio files, which is great for saving space by rough-cutting your large video files taken from a video camera, GoPro, drone, etc. It lets you quickly extract the good parts from your videos and discard many gigabytes of data without doing a slow re-encode and thereby losing quality. There are also many more use cases. Everything is extremely fast because it does an almost direct data copy, fueled by the awesome FFmpeg which does all the grunt work.
Artificial intelligence (AI) is the buzzword today with AI-based applications demonstrating great performance, speed, and accuracy. Their deployment is widespread in domains like healthcare, finance, retail, automotive, manufacturing, logistics, education, agriculture, telecom, travel, insurance, etc (the list includes almost every major field of work).
Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It is built around universal standards, supporting Ollama and OpenAI-compatible Protocols (specifically Chat Completions). This protocol-first approach makes it a powerful, provider-agnostic AI deployment solution for both local and cloud-based models.
Google's aggressive artificial intelligence (AI) push has not slowed down in 2026. The company has already announced a partnership with Apple, released new shopping tools and a protocol, introduced Personal Intelligence in Gemini and added the chatbot to its Trends website. Now, the company has shifted its focus towards the open community with the release of TranslateGemma models. These multilingual AI models are designed to support translation between a large number of languages across text and image (input only) modalities.
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.
Moves that are in the public domain - including: It's a Wonderful Life, Metropolis, All Quiet not he Western Front, The Gold Rush, A Streetcar Named Desire,
A large language model’s parameters are often said to be the dials and levers that control how it behaves. Think of a planet-size pinball machine that sends its balls pinging from one end to the other via billions of paddles and bumpers set just so. Tweak those settings and the balls will behave in a different way.
We aren’t just using these AI tools as assistant anymore; they’re fixing code bugs on their own, making full movies from a sentence, and staying focused for days without forgetting the plan. We went from having helpful assistants to creating actual digital coworkers in less than a year.
This chart made up of millions of PerformanceTest benchmark results and is updated daily with new graphics card benchmarks. This high end chart contains high performance video cards typically found in premium gaming PCs. Recently introduced AMD video cards and nVidia graphics cards using the PCI-Express (or PCI-E) standard are common in our high end video card charts.
NVIDIA today announced the NVIDIA Nemotron™ 3 family of open models, data and libraries designed to power transparent, efficient and specialized agentic AI development across industries.
Here you will quickly learn all about local LLM hardware, software & models to try out first. There are many reasons why one might try to get into local large language models. One is wanting to own a local and fully private, personal AI assistant. Another is a need for a capable roleplay companion or story writing helper. Whatever your goal is, this guide will walk you through the basics of local LLMs including hardware requirements, inference software options, and lightweight models to start with. Enjoy!
