# NVIDIA

parakeet-tdt-0.6b-v2 is a 600 million parameter automatic speech recognition (ASR) model designed to achieve high-quality English transcription with accurate timestamp prediction and automatic punctuation and capitalization support. The model is based on the FastConformer architecture, capable of efficiently processing audio clips up to 24 minutes long, making it suitable for developers, researchers, and various industry applications.

NVIDIA Project DIGITS is a desktop supercomputer based on the NVIDIA GB10 Grace Blackwell superchip, designed to provide robust AI performance for AI developers. It delivers AI performance of up to 10 petaflops in an efficient, compact form factor. The product comes pre-installed with the NVIDIA AI software stack and features 128GB of memory, enabling developers to prototype, fine-tune, and infer large AI models with up to 200 billion parameters locally, and seamlessly deploy them to data centers or the cloud. The launch of Project DIGITS marks another significant milestone in NVIDIA's commitment to advancing AI development and innovation, offering developers a powerful tool to accelerate the development and deployment of AI models.

Sana is a text-to-image generation framework developed by NVIDIA, designed to efficiently generate images with resolutions of up to 4096×4096 pixels. Notable for its rapid performance and strong text-image alignment capabilities, Sana can be deployed on laptop GPUs, marking a significant advancement in image generation technology. The model is based on a linear diffusion transformer and utilizes a pre-trained text encoder along with a spatially compressed latent feature encoder to generate and modify images based on text prompts. The open-source code for Sana is available on GitHub, with promising research and application prospects, particularly in areas like art creation, educational tools, and model research.

Sana is a text-to-image generation framework developed by NVIDIA, capable of efficiently producing images up to 4096×4096 resolution. With its rapid processing speed and robust text-image alignment capabilities, it can even be deployed on laptop GPUs. It is based on a linear diffusion transformer (text-to-image generative model) with 1648M parameters, specifically designed for generating multi-scale images at a base resolution of 1024px. Key advantages of the Sana model include high-resolution image generation, rapid synthesis speed, and strong text-image alignment capabilities. The model's background reveals that it is developed using open-source code, available on GitHub, and adheres to specific licensing (CC BY-NC-SA 4.0 License).

Sana is a text-to-image framework developed by NVIDIA, capable of efficiently generating images with resolutions up to 4096×4096. It synthesizes high-resolution, high-quality images at remarkable speeds while maintaining robust text-image alignment, making it deployable on laptop GPUs. The Sana model is based on linear diffusion transformers, utilizing pre-trained text encoders and spatially compressed latent feature encoders, supporting Emoji, Chinese, and English inputs, as well as mixed prompts.

Sana is a text-to-image framework developed by NVIDIA, capable of efficiently generating images with resolutions up to 4096×4096. It synthesizes high-resolution, high-quality images at an extremely fast speed, featuring strong text-image alignment capabilities and deployable on laptop GPUs. The model is based on linear diffusion transformers, utilizing a fixed pre-trained text encoder and a space-compressed latent feature encoder, supporting mixed prompts in English, Chinese, and emojis. The key advantages of Sana include high efficiency, high-resolution image generation capability, and multilingual support.

Sana is a text-to-image generation framework developed by NVIDIA that efficiently produces high-definition images with resolutions of up to 4096×4096. It maintains high text-image consistency and operates at high speed, making it deployable on laptop GPUs. The Sana model is based on linear diffusion transformers and uses pre-trained text encoders along with spatially compressed latent feature encoders. This technology is significant for its ability to rapidly generate high-quality images, having a revolutionary impact on artistic creation, design, and other creative fields. The Sana model is licensed under CC BY-NC-SA 4.0, and its source code is available on GitHub.

Sana is a text-to-image generation framework developed by NVIDIA, capable of efficiently generating images with resolutions up to 4096×4096. Known for its speed, strong text-image alignment capabilities, and deployability on laptop GPUs, Sana is built on a linear diffusion transformer, utilizing pre-trained text encoders and spatially compressed latent feature encoders, representing the latest advancements in text-to-image generation technology. Sana's key advantages include high-resolution image generation, fast synthesis, deployability on laptop GPUs, and open-source code, providing significant value in both research and practical applications.

Sana-1.6B is an efficient high-resolution image synthesis model based on linear diffusion transformer technology, capable of generating high-quality images. Developed by NVIDIA Labs, it employs DC-AE technology and boasts a potential space of 32 times, allowing it to run on multiple GPUs and deliver powerful image generation capabilities. Renowned for its efficient image synthesis and high-quality output, Sana-1.6B is a significant technology in the image synthesis field.

Star-Attention is a novel block-sparse attention mechanism proposed by NVIDIA aimed at improving the inference efficiency of large language models (LLMs) based on Transformers for long sequences. This technology significantly boosts inference speed through a two-stage operation while maintaining an accuracy rate of 95-100%. It is compatible with most Transformer-based LLMs, allowing for direct use without additional training or fine-tuning, and can be combined with other optimization methods such as Flash Attention and KV cache compression techniques to further enhance performance.

Fugatto (short for Foundational Generative Audio Transformer Opus 1) is a generative AI sound model launched by NVIDIA that can generate or transform any described music, sound, and speech combinations using text and audio inputs. This model can create music segments based on text prompts and add or remove instruments from existing songs, change the accent or emotion of speech, and even allow users to create unprecedented sounds. The launch of Fugatto marks a significant advancement in the field of audio synthesis and transformation, demonstrating new capabilities that emerge from its training.

Llama-3.1-Nemotron-70B-Instruct is a large language model tailored by NVIDIA, focusing on improving the helpfulness of responses generated by large language models (LLM). This model excelled in various auto-alignment benchmark tests, such as Arena Hard, AlpacaEval 2 LC, and GPT-4-Turbo MT-Bench. It is trained using RLHF (specifically, the REINFORCE algorithm), Llama-3.1-Nemotron-70B-Reward, and HelpSteer2-Preference prompts on the Llama-3.1-70B-Instruct model. This model not only showcases NVIDIA's technological advances in enhancing generative support for general domain instructions but also offers a model conversion format compatible with the Hugging Face Transformers library, with free hosted inference available through NVIDIA's build platform.

Llama-3.1-Nemotron-51B is a new language model developed by NVIDIA based on Meta's Llama-3.1-70B. It utilizes neural architecture search (NAS) technology to optimize accuracy and efficiency. The model can run on a single NVIDIA H100 GPU, significantly reducing memory usage, bandwidth, and computational demands while maintaining excellent accuracy. It represents a new balance between accuracy and efficiency in AI language models, providing developers and businesses with a high-performance AI solution that is cost-effective.

The NVIDIA App is designed specifically for PC gamers and creators, enabling users to stay updated with the latest NVIDIA drivers and technology. Through a unified GPU Control Center, users can optimize game and application settings, capture moments with powerful in-game overlay recording tools, and seamlessly discover the latest NVIDIA tools and software.

Llama3-70B-SteerLM-RM is a 70-billion parameter language model that serves as a property prediction model, specifically a multi-faceted reward model. It evaluates model responses across multiple dimensions instead of relying on a single score, unlike traditional reward models. This model was trained on the HelpSteer2 dataset and utilizes NVIDIA NeMo-Aligner, an scalable toolkit for efficient and effective model alignment.

Nemotron-4-340B-Base is a large language model developed by NVIDIA, boasting 340 billion parameters and a context length of 4096 tokens. It is suitable for generating synthetic data and aiding researchers and developers in building their own large language models. The model has been pre-trained on 9 trillion tokens, encompassing over 50 natural languages and 40 programming languages. The NVIDIA open model license permits commercial use and the creation and distribution of derivative models, without claiming ownership of any output generated by using the model or any derived models.

NVIDIA RTX Remix, an open-source modding toolkit launched by NVIDIA, allows creators and game developers to leverage the powerful capabilities of NVIDIA RTX technology to enhance their games and creative projects. Harnessing the power of real-time ray tracing and AI-driven graphics enhancements, RTX Remix delivers stunningly realistic visual experiences to games. Beyond providing a robust platform for creators, RTX Remix fosters innovation in the gaming and creative realms by enabling open API and connector integrations with other applications and services.

NVIDIA ACE offers a suite of advanced generative AI models and microservices that are easy to deploy and performant. These AI models are trained on commercially secure, responsibly licensed data and are secured through fine-tuning and safeguard measures to ensure accurate, appropriate, and relevant results regardless of user input. ACE supports flexible deployment options, enabling deployment and execution on the cloud or NVIDIA RTX AI PCs. Additionally, ACE provides a digital human workflow, allowing developers to integrate ACE NIMs into their products, tools, services, or games for specific AI workflows, such as NPCs and customer service assistants. NVIDIA has partnered with Inworld AI to showcase an example of integrating NVIDIA ACE into an end-to-end NPC platform, which delivers cutting-edge visuals in Unreal Engine 5.

Llama3-ChatQA-1.5-8B is an advanced conversational question-answering and retrieval-augmented generation (RAG) model developed by NVIDIA. Improved upon ChatQA (1.0), it enhances its tabular and arithmetic calculation capabilities by adding conversational question-answering data. It comes in two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B, both trained using Megatron-LM and converted to Hugging Face format. The model excels in the benchmark tests of ChatRAG Bench, suitable for scenarios requiring complex conversational understanding and generation.