Gemma 4 brings powerful reasoning, coding and multimodal AI directly to NVIDIA RTX PCs, DGX Spark and edge devices.

The NVIDIA Developer Blog provides developers with a  knowledge on GPU computing, AI, and deep learning, offering tutorials, code samples, and real-world applications of NVIDIA technologies. From optimizing GPU-accelerated algorithms to implementing  AI models, developers can learn practical techniques and strategies for harnessing the power of NVIDIA GPUs in their projects. Moreover, the blog highlights advancements in GPU architectures, CUDA programming, and GPU-accelerated libraries, empowering developers to stay at the forefront of GPU computing innovation.

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Google's Gemma 4 family of open models — spanning E2B, E4B, 26B, and 31B variants — has been optimized by NVIDIA for local deployment across RTX GPUs, DGX Spark, and Jetson Orin Nano edge devices. The models support reasoning, coding, agentic tool use, multimodal inputs (vision, video, audio), and 35+ languages. Deployment is available via Ollama, llama.cpp with GGUF checkpoints on Hugging Face, and Unsloth Studio for fine-tuning. NVIDIA Tensor Cores and the CUDA stack enable efficient inference without extensive per-device optimization. The models are compatible with OpenClaw for building local AI agents that access personal files and workflows.

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