使用--gpu=max卸载所有可能内容。在独立GPU系统（配备NVIDIA显卡的Linux/Windows）上更为重要，因为GPU显存与系统内存分离。若模型无法完全装入显存，部分卸载（--gpu=0.5）会将层级分配至GPU和CPU，以速度换取运行更大模型的能力。
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Mirroring previous iterations of its accessible models, Google engineered Gemma 4 for operation on personal computing systems. This encompasses various implementation scenarios. The two larger configurations—26B Expert Ensemble and 31B Unified—are built to operate without compression in bfloat16 configuration using a solitary 80GB Nvidia H100 graphics processor. Although this represents a $20,000 specialized computing unit, it remains classified as local equipment. When compressed for reduced precision operations, these substantial models become compatible with mainstream graphics cards.