Different scenarios have varying demands for GPU types. For tasks like model inference or basic operations, a CPU or even on-device solutions (mobile, web) might suffice.
When a GPU is necessary, common choices include T4, 3090, P10, V100, etc., selected based on factors like price, required computing power, and memory capacity.
Model training also has diverse needs based on the specific task. For basic, general-purpose vision tasks, 1 to 50 cards like the 3090 might be enough. However, cutting-edge areas like visual generation and LLMs often require A100s or A800s, scaling from 1 to even thousands of cards.
When a GPU is necessary, common choices include T4, 3090, P10, V100, etc., selected based on factors like price, required computing power, and memory capacity.
Model training also has diverse needs based on the specific task. For basic, general-purpose vision tasks, 1 to 50 cards like the 3090 might be enough. However, cutting-edge areas like visual generation and LLMs often require A100s or A800s, scaling from 1 to even thousands of cards.