The amount of VRAM needed for machine learning tasks depends on the complexity of the models and the size of the datasets. For basic tasks, such as small neural networks or tabular data, 4–6 GB of VRAM is usually sufficient.
For deep learning tasks, especially with large models like transformers or CNNs, 8–16 GB of VRAM is recommended. Tasks like training large datasets (e.g., ImageNet) or fine-tuning pre-trained models benefit from GPUs with 24 GB or more VRAM.
High-end GPUs like NVIDIA RTX 3090 or A100 are ideal for intensive workloads, ensuring smooth processing and faster training times. Selecting the right VRAM capacity depends on the project’s scale and requirements.