Will there be a significant increase in the need for computing capacity for inference when sophisticated multimodal models (such as robotics) become more widely used? Assume that every home has a robotic helper. Although there will still be a lot of compute used for training, is a spike in the need for inference compute power realistic?
What is the trade-off between advanced multimodal model inference and GPU/CPU?
The trade off between advanced multimodal model inference and GPU/CPU resources is substantial. While GPUs are typically more effective for inference due to their parallel processing power, increasing model complexity can still create bottlenecks, even for GPUs.
I’ve been thinking about this as well. From my experience working with AI, I believe there will indeed be a significant increase in the need for computing capacity for inference as multimodal models, especially in robotics, become more common. Training sophisticated models is resource-intensive, but inference, especially if performed locally in every home, will require substantial computational power to handle real-time processing and interactions. The trade-off between advanced multimodal model inference and GPU/CPU usage will likely involve balancing performance and cost. GPUs generally offer higher performance for inference tasks compared to CPUs but can be more expensive. Optimizing these models to run efficiently on available hardware while managing costs will be crucial as the technology becomes more widespread.