Want to make the most of the new Gemma 4 AI models? RTX GPUs and PCs accelerate local AI like never before
With the launch of Google’s Gemma 4 family of AI models, AI enthusiasts now have access to a new class of small, fast, omni-capable AIs designed for rapid and efficient local deployment, and NVIDIA RTX GPUs can accelerate them to great effect. Google and NVIDIA have worked closely to optimize the Gemma 4 models for NVIDIA RTX-equipped PCs and workstations, such as the NVIDIA DGX Spark personal AI supercomputer and NVIDIA Jetson Orin Nano.
Its incredible local AI capabilities make it ideal for running on an RTX PC powered by NVIDIA GeForce RTX graphics. Leading GPUs like the NVIDIA GeForce RTX 5090 for consumers, the NVIDIA RTX 5000 for professionals, and the NVIDIA DGX Spark for enthusiasts and the most serious AI developers deliver the high-speed AI hardware to run these cutting-edge models, and the performance-enhanced Tensor cores to run them at maximum speed for the lowest latency responses.
Gemma 4 models run on llama.cpp and Ollama with RTX optimizations, enabling fast and responsive local AI performance.
RTX PCs enable faster inference on Gemma 4
Google’s Gemma 4 models are designed to provide robust problem-solving reasoning, fast and efficient code generation and debugging capabilities, support for using agent tools, and advanced video and audio capabilities. They also offer multilingual support so they can be used by anyone anywhere in the world.
But you only get the full capabilities of the Gemma 4 models when you run them on NVIDIA RTX GPUs. When you run Gemma 4-31B on an NVIDIA RTX 5090, you can unlock nearly three times the performance compared to powerful alternatives, like the MacBook M3 Ultra. Smaller models are also improved, with the Gemma 4-26B-A4B and Gemma 4-E4B also showing more than twice the performance improvements when upgrading to an RTX 5090.
Nvidia
Fully compatible with OpenClaw, Gemma 4 templates allow users to create fast, high-performance local agents that leverage local files to respond to user requests within local applications and automated workloads. When running on NVIDIA RTX graphics hardware, you can rest assured that these agents are running at peak performance and efficiency.
Accelerated focusing
One of the biggest benefits of running local AI models on your own hardware is faster fine-tuning. Fine-tuning allows you to retrain a model with your own data, taking it from a powerful general-purpose tool and transforming it into a tailor-made device for your specific workflows. This allows you to improve the quality of responses and tailor the results to your business needs.
NVIDIA offers the best support for this process with popular tools, all built on PyTorch and optimized for NVIDIA RTX GPUs. With Gemma 4 models, you get the most advanced local AI for reasoning and coding, but with fine-tuning supported by NVIDIA, you can customize it exactly to your use cases.
Ready from day 0
AI developments are coming at a rapid pace and it can be difficult to keep track of what’s coming and what’s already been launched. One of the best ways to ensure you’re always ready to take advantage of the latest developments in local AI models is to have an NVIDIA RTX GPU on hand and ready to go.
NVIDIA’s RTX 50 Series graphics cards have enough VRAM to load the Gemma 4 models and a range of others. Their Tensor Cores help accelerate AI workloads for faster training and inference, and CUDA-enabled toolkits give you full control to select models, change quantizations, modify parameters, or run your own workflows.
With local AI running on an RTX PC, you get support for the most advanced AI models and features, helping you take advantage of the latest AI today and prepare for what’s coming tomorrow.
Improved memory performance with RTX GPUs
A key part of developing the most effective local AI models is optimizing memory efficiency. While cloud computing data centers can continually increase model sizes, local AI models must be more efficient. This is why NVIDIA has been at the center of memory optimization for local AI models for years.
NVIDIA pioneered the RTX-exclusive acceleration of NVFP4, a floating-point format that reduces VRAM consumption by up to 60% on NVIDIA GPUs based on the Blackwell architecture. When powered by NVIDIA’s fifth-generation Tensor Cores, AI acceleration reaches new performance heights. The latest GPUs can handle tasks in a fraction of the time compared to even very powerful alternatives, like Apple’s next-generation MacBooks.
Why RTX is best for local AI
Although the most successful AI models will likely always need to rely on the power of scalable cloud computing, running AI locally has incredible strengths that cannot be overlooked.
Where data privacy is of paramount importance, running AI locally ensures that data never leaves your system, keeping sensitive information completely within your control. For organizations and individuals dealing with sensitive data, using a local AI solution running on an NVIDIA RTX GeForce graphics card is the best way to secure it. This is doubly important if you’re using agentic AI to do tasks on your PC for you.
When you run an AI model locally, it’s easier to provide it with all the contextual data it needs. Instead of uploading terabytes of information to the cloud, where privacy concerns arise and network interference can waste hours, local AI has everything it needs right away, and fine-tuning tracking is also easier and more efficient.
Even as a workplace transformation tool, the costs associated with AI still need to be tracked and measured: tokens should lead to increased productivity and profitability. Relying on AI running locally on your own RTX hardware ensures you can manage costs every step of the way, from initial purchase to deployment and ongoing maintenance. No need for Cloud AI subscriptions or long-term token fees. Simply supply the power and your powerful NVIDIA GeForce RTX AI graphics card will take care of the rest.
NVIDIA also offers a wide range of AI-enabled RTX 50 series graphics cards. All Blackwell graphics cards are built with the latest generation of AI accelerator Tensor cores for advanced AI capabilities. Alongside flagship cards like the RTX 5090 and its professional counterpart, the RTX PRO 6000, the RTX 5080 is also a powerful card for local AI development and tuning.
