Gemma 4 trips on 12GB VRAM

This is the classic “small model, big serving footprint” trap. Parameter count alone does not tell you whether a model will fit comfortably in a real inference stack, especially once KV cache and engine buffers enter the picture.

• –Google pitches Gemma 4 E2B/E4B for edge and on-device use, but the practical path on consumer GPUs is usually quantized or lower-memory serving, not default BF16 vLLM
• –An 8192-token context materially increases VRAM pressure, so a 12GB mobile card can run out of room before the first prompt
• –Claims of 26B-on-12GB setups usually depend on aggressive quantization, shorter context windows, CPU offload, or a different runtime with a smaller memory footprint
• –The likely fixes are to reduce max model length, lower GPU memory utilization, switch to a quantized checkpoint, or use a runtime better suited to constrained VRAM
• –The broader signal is that “runs on laptop GPU” and “runs in vLLM with full server defaults” are not the same thing