Multimodal search systems, which handle diverse data types like text, images, audio, and video, require significant computational resources due to the complexity of processing and combining multiple modalities. At a high level, these systems need robust processing power, efficient storage for embeddings, and scalable infrastructure to handle real-time queries. For example, processing an image through a neural network to generate a vector embedding demands GPUs or TPUs to accelerate matrix operations, while text analysis might rely on transformers like BERT. Storage must accommodate high-dimensional vectors (e.g., 512- or 1024-dimensional embeddings) for millions of items, which can quickly grow to terabytes. Query handling requires low-latency retrieval, often using approximate nearest neighbor (ANN) libraries like FAISS or HNSW to balance speed and accuracy.
The computational burden grows with the complexity of multimodal models. Combining modalities—such as aligning text and images in systems like CLIP—requires training large neural networks that fuse data streams, which is resource-intensive. Training such models often involves distributed computing across multiple GPUs or nodes to manage memory and speed. For instance, fine-tuning a pre-trained multimodal model on custom data might take days on a cluster of GPUs. Even during inference, real-time systems must process inputs in parallel; a video search system might split frames for GPU processing while simultaneously analyzing audio with a separate model. Optimizing these pipelines often involves frameworks like TensorFlow Serving or ONNX Runtime to reduce latency. Developers must also manage memory constraints—loading multiple large models (e.g., ResNet for images and Whisper for audio) into memory simultaneously can strain server resources.
Scalability and latency are critical challenges. As the dataset grows, indexing and searching billions of embeddings require distributed databases like Elasticsearch or Milvus, which partition data across nodes. For example, a product search system combining text descriptions and product images might shard embeddings by category to speed up queries. Latency is minimized through caching frequent queries, pruning redundant model layers, or using quantization to shrink embedding sizes. Preprocessing steps, such as resizing images or filtering noise from audio, can reduce compute overhead before data reaches models. Edge computing is another consideration: deploying lightweight models (e.g., MobileNet for images) on edge devices reduces server load for applications like mobile visual search. Balancing these trade-offs—accuracy vs. speed, centralized vs. distributed processing—is key to building efficient multimodal systems.