Open models are leading the way for on-device AI, bringing innovation to everyday devices beyond the cloud. As these models progress, their effectiveness relies heavily on access to local, real-time context that can translate insights into action.
Google has introduced new additions to the Gemma 4 family, designed to cater to this shift. These models are small, fast, and versatile, built for efficient local execution across a wide range of devices.
In collaboration with NVIDIA, Google has optimized Gemma 4 for NVIDIA GPUs, ensuring efficient performance across various systems, from data center deployments to NVIDIA RTX-powered PCs and workstations, the NVIDIA DGX Spark personal AI supercomputer, and NVIDIA Jetson Orin Nano edge AI modules.
The Gemma 4 family includes E2B, E4B, 26B, and 31B variants, all tailored for efficient deployment from edge devices to high-performance GPUs. These models support a range of tasks including reasoning, coding, agents, vision, video, and audio capabilities, interleaved multimodal input, and multilingual support.
The E2B and E4B models are optimized for ultra-efficient, low-latency inference at the edge, running offline with minimal latency on devices like Jetson Nano modules. On the other hand, the 26B and 31B models are geared towards high-performance reasoning and developer-centric workflows, making them ideal for agentic AI.
As local agentic AI gains traction, applications like OpenClaw are enabling always-on AI assistants on RTX PCs, workstations, and DGX Spark. The latest Gemma 4 models are compatible with OpenClaw, empowering users to build capable local agents that automate tasks by drawing context from personal files, applications, and workflows.
To utilize Gemma 4 locally, users can download Ollama to run the models or install llama.cpp and pair it with the Gemma 4 GGUF Hugging Face checkpoint. Unsloth also offers support with optimized and quantized models for efficient local fine-tuning and deployment via Unsloth Studio.
Running Gemma 4 models on NVIDIA GPUs ensures optimal performance, with NVIDIA Tensor Cores accelerating AI inference workloads to enhance throughput and reduce latency for local execution. The CUDA software stack ensures compatibility across leading frameworks and tools, enabling new models to run efficiently without extensive optimization.
The versatility of open models like Gemma 4 allows them to scale across a wide range of systems, from Jetson Orin Nano at the edge to RTX PCs, workstations, and DGX Spark, without the need for extensive optimization.
For more information on getting started with Gemma 4 on NVIDIA GPUs, visit the NVIDIA technical blog. Stay updated with NVIDIA’s work on open models by checking out their blog.
In case you missed it, NVIDIA has made several updates for RTX AI PCs, including the introduction of NVIDIA NemoClaw, an open-source stack that optimizes OpenClaw experiences on NVIDIA devices. Accomplish.ai also announced Accomplish FREE, a no-cost version of its open-source desktop AI agent that utilizes NVIDIA GPUs for local execution.
Stay connected with NVIDIA AI PC on various social media platforms and subscribe to the RTX AI PC newsletter for the latest updates. Follow NVIDIA Workstation on LinkedIn and X for more insights.
In conclusion, the collaboration between Google and NVIDIA has brought forth a new wave of on-device AI with the Gemma 4 family, offering compact models optimized for NVIDIA GPUs. These models are set to revolutionize local AI execution on everyday devices, bringing innovation closer to the edge.
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