NVIDIA Accelerates Gemma 4 for Local Agentic AI

Open models are driving a new wave of on-device AI, extending innovation beyond the cloud to everyday devices. As these models advance, their value increasingly depends on access to local, real-time context that can turn meaningful insights into action. Designed for this shift, Google’s latest additions to the Gemma 4 family introduce a class of small, fast and omni-capable models built for efficient local execution across a wide range of devices.

Google and NVIDIA have collaborated to optimize Gemma 4 for NVIDIA GPUs, enabling efficient performance across a range of 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 latest additions to the Gemma 4 family, spanning E2B, E4B, 26B and 31B variants, are designed for efficient deployment from edge devices to high-performance GPUs.

All configurations were measured using Q4_K_M quantizations. This new generation of compact models supports a range of tasks, including strong performance on complex problem-solving tasks, code generation and debugging for developer workflows, and native support for structured tool use. The E2B and E4B models are built for ultra-efficient, low-latency inference at the edge, running completely offline with near-zero latency across many devices including Jetson Nano modules.

The 26B and 31B models are designed for high-performance reasoning and developer-centric workflows, making them well suited for agentic AI. Optimized to deliver state-of-the-art, accessible reasoning, these models run efficiently on NVIDIA RTX GPUs and DGX Spark — powering development environments, coding assistants, and agent-driven workflows.

As local agentic AI continues to gain momentum, 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, allowing users to build capable local agents that draw context from personal files, applications, and workflows to automate tasks.