Gemma 4 vs Phi-4-mini vs Llama 3.2: VRAM Requirements Compared

This benchmark directly addresses one of the most critical challenges in local LLM deployment: understanding which models fit within specific hardware constraints. The comparison of Gemma 4, Phi-4-mini, and Llama 3.2 across VRAM requirements from 3GB to 16GB provides practical guidance for practitioners working with limited resources—from edge devices to entry-level GPUs.

The significance lies in the breadth of options now available for resource-constrained environments. Phi-4-mini at 3GB opens possibilities for genuinely edge-deployable AI, while Gemma 4 at 16GB targets more capable consumer hardware. This diversity means developers can match model capability directly to their deployment target without accepting unnecessary trade-offs.

For practitioners, this benchmark is essential reference material for hardware selection and model-to-device matching. As quantisation techniques continue evolving, understanding baseline VRAM footprints helps predict performance and feasibility before implementation.

Read the full article on tech-insider.org.

Source: tech-insider.org · Relevance: 9/10