Part 6/11:
After initial testing, the creator sat down with Nvidia representatives, who clarified some key points:
Hardware design favors training and multi-model workloads over raw inference speed. The device's 128 GB of shared VRAM allows for multi-agent AI operations, running multiple models concurrently—something Terry struggles with because of VRAM limitations.
Larry's strength truly lies in longer, more complex AI workflows like training and fine-tuning, where large unified memory becomes a game-changer. It can handle training models up to 200 billion parameters, something impossible on typical consumer GPUs.