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NVIDIA collaborates with Google DeepMind to drive LLM innovation

NVIDIA collaborates with Google DeepMind to drive LLM innovation


With large-scale language models powering generative AI, powerful innovation models that process multiple types of data, such as text, images, and audio, are becoming increasingly common.

However, building and deploying these models remains difficult. Developers need a way to quickly experience and evaluate models to determine which is best for their use case, and optimize model performance in a way that is not only cost-effective but also provides the best performance. I am.

To make it easier for developers to create AI-powered applications with world-class performance, NVIDIA and Google today announced three new collaborations at Google I/O 24.

Gemma + NIM

NVIDIA is working with Google using TensorRT-LLM to optimize two new models announced at the event: Gemma 2 and PaliGemma. These models are built on the same research and technology used to create the Gemini model, and each focuses on a specific area.

Gemma 2 is the next generation Gemma model for a wide range of use cases, with an all-new architecture designed for breakthrough performance and efficiency. PaliGemma is an Open Vision Language Model (VLM) inspired by PaLI-3. Built on open components, including the SigLIP vision model and the Gemma language model, PaliGemma provides a vision language for captioning images and short videos, visual question answering, understanding text in images, object detection, object segmentation, and more. Designed for the task. PaliGemma is designed for best-in-class fine-tuning performance across a wide range of visual language tasks and is also supported by NVIDIA JAX-Toolbox.

Gemma 2 and PaliGemma are delivered with NVIDIA NIM inference microservices, part of the NVIDIA AI Enterprise Software Platform, to simplify the deployment of AI models at scale. NIM support for two new models is available from PaliGemma today in the API Catalog. These will soon be released as containers on NVIDIA NGC and GitHub.

Bringing accelerated data analytics to Colab

Google also announced that RAPIDS cuDF, an open source GPU dataframe library, is now supported by default in Google Colab, one of the most popular developer platforms for data scientists. Google Colabs' 10 million monthly users can now use NVIDIA L4 Tensor Core GPUs to speed up pandas-based Python workflows by up to 50x in just seconds, without any code changes.

RAPIDS cuDF enables developers using Google Colab to accelerate exploratory analysis and production data pipelines. Although pandas is one of the most popular data processing tools in the world due to its intuitive API, applications often become difficult as data size increases. Even with 5-10 GB of data, many simple operations can take several minutes to complete on the CPU, slowing down exploratory analysis and production data pipelines.

RAPIDS cuDF is designed to solve this problem by seamlessly accelerating Pandas code on the GPU when applicable, and falling back to CPU-Pandas when not. RAPIDS cuDF, available by default in Colab, brings high-speed data analytics to every developer, everywhere.

Putting AI on the road

With AI PCs with NVIDIA RTX graphics, Google and NVIDIA are making it easy for app developers to integrate generative AI models, like the new Gemma model family, into web and mobile applications to deliver custom content. We also announced a Firebase Genkit collaboration that will enable you to distribute and provide semantics. Find and answer questions. Developers can start their work streams using local RTX GPUs before seamlessly moving their work to Google Cloud infrastructure.

To make this even easier, developers can build apps with Genkit using JavaScript, a programming language commonly used by mobile developers to build apps.

The beat of innovation continues

NVIDIA and Google Cloud are collaborating across multiple domains to advance AI. From supporting the DGX Cloud platform and JAX framework leveraging the upcoming Grace Blackwell to bringing the NVIDIA NeMo framework to Google Kubernetes Engine, the two companies' full-stack partnership will leverage his NVIDIA technology on Google Cloud. The possibilities of what customers can do with AI expand.




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