CUDA and OpenCL are both frameworks for GPU computing, but CUDA is purpose-built for NVIDIA GPUs while OpenCL is an open standard that targets a wide range of devices. Because CUDA is tightly integrated with NVIDIA hardware, it offers deeper control over memory, thread scheduling, and hardware-specific optimizations. This tends to make CUDA faster and easier to optimize on NVIDIA GPUs, which is why it is widely used in machine learning, simulation, and scientific workloads. CUDA also has a richer ecosystem of libraries, tools, and documentation.
OpenCL’s main advantage is portability across different hardware vendors, including CPUs, GPUs, and FPGAs. However, this generality means developers must often write more boilerplate code and cannot take full advantage of NVIDIA-specific optimizations. CUDA’s runtime, profiling tools, and compiler stack are more integrated and easier to use for complex workloads. For developers who primarily work on NVIDIA hardware—which includes most machine learning and scientific computing environments—CUDA is generally the more practical choice.
In the context of GPU-accelerated vector search, most systems—including Milvus and Zilliz Cloud—build their GPU acceleration layers on CUDA rather than OpenCL, because CUDA provides optimized primitives for numerical workloads and better tooling for debugging and tuning performance. This makes CUDA a stronger fit for applications involving large-scale vector comparisons, embeddings, and ANN search. While OpenCL can still serve as a cross-platform alternative, CUDA remains the preferred option when maximum performance on NVIDIA GPUs is the priority.