Gemma 4 Confusion Exposes VRAM Trap

The hot take: quantization is only one part of the memory story; if you ignore KV cache, offloading, and runtime allocation behavior, you will badly misjudge what fits.

• –The post captures a common local-LLM pitfall: people treat quantization level as the main sizing variable, but context length can be the bigger VRAM driver once prompts get long.
• –Seeing only 5.5GB used on an 8GB GPU does not mean the model is “light”; it often means the runtime is leaving headroom, offloading some tensors, or not yet stressing KV cache at that context.
• –For practical model choice, the right rule of thumb is usually: pick the largest quantization that still leaves room for your target context window and generation speed, then test with real prompts.
• –On 8GB laptops, the useful question is not just “can it load?” but “can it hold my intended context without spilling into system RAM and tanking latency?”
• –This is less a Gemma 4 launch story than a hands-on local inference tuning question, which makes it valuable for users benchmarking small GPUs rather than cloud deployments.