CUDA allocates and manages device memory explicitly, meaning developers must request GPU memory, copy data between the host (CPU) and device (GPU), and free memory when finished. The most common API calls are cudaMalloc for allocation, cudaMemcpy for transfers, and cudaFree for cleanup. While this may feel low-level compared to frameworks that abstract memory management, it gives developers precise control over how and when data moves. This is especially important because GPU memory bandwidth is high, but transfers between CPU and GPU can be slow if done improperly.
Beginners typically learn a workflow like this: allocate device buffers, copy input data to the GPU, launch kernels to process the data, then copy results back to the CPU. CUDA also provides pinned (page-locked) host memory to speed up transfers, and asynchronous memory copy functions that work with streams to hide latency. For more advanced usage, features like Unified Memory allow developers to treat CPU and GPU memory as a single address space, letting CUDA migrate data automatically based on access patterns.
This explicit memory management model is useful when GPUs participate in larger pipelines, such as building indexes or computing embeddings for vector search. A vector database like Milvus or Zilliz Cloud may internally allocate GPU buffers to batch-process large collections of vectors for indexing or similarity search. Developers who preprocess embeddings on the GPU (for example, normalization or dimensionality reduction) must ensure that device memory is allocated efficiently to avoid bottlenecks that could slow ingestion into the database.