Gemma 4 strains local RAM

This is a small support thread, but it points at a real local-inference pain point: “edge optimized” does not always mean “fits cleanly in VRAM” once runtimes, KV cache, multimodal components, and backend limitations enter the picture.

• –Gemma 4 E4B is listed by Ollama as 4.5B effective parameters but 8B with embeddings, so the memory profile is not as simple as “4B model equals tiny footprint.”
• –Ollama’s Gemma 4 page shows E4B has 42 layers, 128K context, text/image/audio support, and extra vision/audio encoder parameters, all of which complicate memory budgeting.
• –The Reddit explanation argues llama.cpp-derived stacks such as Ollama and LM Studio may keep inactive or less GPU-friendly parts in system RAM instead of treating storage, RAM, and VRAM the way mobile-first deployment might.
• –For developers, the practical fix is usually to lower context, use a smaller or more aggressively quantized variant, check how many layers are actually offloaded, and compare against a runtime with better Gemma 4-specific support.