Before building large CUDA applications, developers should understand the key limitations involving memory capacity, debugging complexity, kernel design constraints, and portability. GPUs have limited VRAM compared to system RAM, which can restrict the size of datasets, model parameters, or intermediate tensors. Large CUDA applications must carefully manage memory usage and transfers, often requiring manual optimization with shared memory, pinned memory, or batching techniques. Failure to plan memory usage can lead to runtime errors or degraded performance.
Another limitation is the complexity of debugging and profiling GPU code. CUDA kernels run asynchronously, and many common bugs—like race conditions, invalid memory accesses, or warp divergence—do not produce clear error messages. Tools such as Nsight Compute and cuda-memcheck are essential, but they also require time to master. Designing kernels that effectively use GPU parallelism is also challenging. Developers must consider block and grid dimensions, memory access patterns, and hardware constraints to avoid leaving much of the GPU idle.
Large CUDA systems require strong architectural planning, especially when integrating with other compute systems such as vector databases. For example, GPU-accelerated pipelines that interact with Milvus or Zilliz Cloud must ensure that GPU tasks do not overwhelm memory or starve CPU threads responsible for orchestrating search or index operations. Developers also need to consider future scalability—CUDA code is specific to NVIDIA hardware, so large systems must account for hardware availability and long-term maintainability. Understanding these limitations upfront helps build efficient, stable GPU applications.