How do Vision-Language Models manage computational costs during training?

Vision-Language Models (VLMs) manage computational costs during training through a combination of architectural optimizations, efficient data handling, and distributed computing strategies. These models, which process both images and text, face high computational demands due to their large size and multimodal inputs. To address this, developers prioritize techniques like pretraining components separately, using parameter-efficient architectures, and leveraging hardware optimizations. By focusing on these areas, VLMs reduce memory usage, accelerate training, and lower costs without sacrificing performance.

One key approach is optimizing the model architecture. Many VLMs reuse pretrained components—such as vision encoders (e.g., ViT) and language models (e.g., BERT)—and freeze parts of these networks during training. For example, CLIP trains a vision encoder and text encoder jointly but keeps their pretrained weights fixed initially, reducing backpropagation overhead. Cross-attention layers, which connect visual and textual features, are often lightweight and sparsely updated. Techniques like adapter layers or LoRA (Low-Rank Adaptation) further minimize trainable parameters by inserting small, trainable modules into frozen base models. This modular design avoids retraining entire networks from scratch, cutting computation time significantly.

Efficient data processing and distributed training also play critical roles. Image data is often downsampled or compressed (e.g., resizing to 224x224 pixels) to reduce input size. Text is tokenized with subword methods (e.g., Byte-Pair Encoding) to limit sequence lengths. Frameworks like PyTorch and TensorFlow enable distributed training across GPUs or TPUs using data parallelism (splitting batches across devices) or model parallelism (dividing layers across devices). Mixed-precision training (combining FP16 and FP32) speeds up computations while using less memory. Additionally, gradient checkpointing recomputes intermediate activations during backpropagation instead of storing them, trading compute for memory savings. For instance, training models like Flamingo or BLIP-2 uses these strategies to scale efficiently across hundreds of GPUs, balancing speed and resource constraints.