Google’s Open Source Models Gemma Optimized to Run on NVIDIA GPUs

NVIDIA, in collaboration with Google, on Wednesday, launched optimizations across all NVIDIA AI platforms for Gemma — Google’s state-of-the-art new lightweight 2 billion– and 7 billion-parameter open language models that can be run anywhere, reducing costs and speeding innovative work for domain-specific use cases.

Teams from Google and NVIDIA closely worked together to accelerate the performance of Gemma — built from the same research and technology used to create the Gemini models — with NVIDIA TensorRT-LLM, an open-source library for optimizing large language model inference, when running on NVIDIA GPUs in the data center, in the cloud and on PCs with NVIDIA RTX GPUs.

This allows developers to target the installed base of over 100 million NVIDIA RTX GPUs available in high-performance AI PCs globally.

Developers can also run Gemma on NVIDIA GPUs in the cloud, including on Google Cloud’s A3 instances based on the H100 Tensor Core GPU and soon, NVIDIA’s H200 Tensor Core GPUs — featuring 141GB of HBM3e memory at 4.8 terabytes per second — which Google will deploy this year.

Enterprise developers can additionally take advantage of NVIDIA’s rich ecosystem of tools — including NVIDIA AI Enterprise with the NeMo framework and TensorRT-LLM — to fine-tune Gemma and deploy the optimized model in their production application.

Learn more about how TensorRT-LLM is revving up inference for Gemma, along with additional information for developers. This includes several model checkpoints of Gemma and the FP8-quantized version of the model, all optimized with TensorRT-LLM.

Interested decelioers/users can experience Gemma 2B and Gemma 7B directly from their browser on the NVIDIA AI Playground.

Gemma Coming to Chat With RTX

Adding support for Gemma soon is Chat with RTX, an NVIDIA tech demo that uses retrieval-augmented generation and TensorRT-LLM software to give users generative AI capabilities on their local, RTX-powered Windows PCs.

The Chat with RTX lets users personalize a chatbot with their own data by easily connecting local files on a PC to a large language model.

Since the model runs locally, it provides results fast, and user data stays on the device. Rather than relying on cloud-based LLM services, Chat with RTX lets users process sensitive data on a local PC without the need to share it with a third party or have an internet connection.

Google Introduces GEMMA, An Open Source Lightweight GenAI Models That Can Be Run Anywhere

Google has just introduced a new generation of open models to assist developers and researchers in building AI responsibly — Gemma, a family of lightweight, state-of-the art open models built from the same research and technology used to create Google’s earlier 'Gemini' models.

Google says that Gemma models achieved exceptional benchmark results at its 2B and 7B sizes, even outperforming some larger open models.

Developed by Google DeepMind and other teams across Google, Gemma is inspired by Gemini, and the name reflects the Latin gemma, meaning “precious stone.”

Google is also releasing tools to support developer innovation, foster collaboration, and guide responsible use of Gemma models.

Gemma is available worldwide, starting today. Here are the key details to know:

• Google is releasing model weights in two sizes: Gemma 2B and Gemma 7B. Each size is released with pre-trained and instruction-tuned variants.
• Toolchains for inference and supervised fine-tuning (SFT) across all major frameworks: JAX, PyTorch, and TensorFlow through native Keras 3.0.
• Ready-to-use Colab and Kaggle notebooks, alongside integration with popular tools such as Hugging Face, MaxText, NVIDIA NeMo and TensorRT-LLM, make it easy to get started with Gemma.
• Pre-trained and instruction-tuned Gemma models can run on your laptop, workstation, or Google Cloud with easy deployment on Vertex AI and Google Kubernetes Engine (GKE).
• Optimization across multiple AI hardware platforms ensures industry-leading performance, including NVIDIA GPUs and Google Cloud TPUs.
• Terms of use permit responsible commercial usage and distribution for all organizations, regardless of size.

In addition, Google has also released a new Responsible Generative AI Toolkit together with Gemma to help developers and researchers prioritize building safe and responsible AI applications. The toolkit includes:

• Safety classification: We provide a novel methodology for building robust safety classifiers with minimal examples.
• Debugging: A model debugging tool helps you investigate Gemma's behavior and address potential issues.
• Guidance: You can access best practices for model builders based on Google’s experience in developing and deploying large language models.

Free credits for research and development

Gemma is built for the open community of developers and researchers powering AI innovation. You can start working with Gemma today using free access in Kaggle, a free tier for Colab notebooks, and $300 in credits for first-time Google Cloud users. Researchers can also apply for Google Cloud credits of up to $500,000 to accelerate their projects.