Yes, vector search can run on edge hardware like the NVIDIA Jetson. The Jetson series, designed for AI and edge computing, provides sufficient compute power for vector search tasks through its GPU-accelerated architecture. Vector search relies on comparing high-dimensional vectors (often generated by machine learning models) to find similarities, which requires efficient handling of matrix operations and nearest-neighbor algorithms. The Jetson’s CUDA cores and support for libraries like CUDA, cuDNN, and TensorRT make it possible to run these computations locally, even with limited resources. For example, lightweight vector databases such as FAISS (Facebook AI Similarity Search) or Milvus Lite can be optimized to leverage the Jetson’s GPU, enabling real-time search without relying on cloud infrastructure.
Developers can implement vector search on Jetson devices by using frameworks optimized for edge hardware. FAISS, for instance, offers GPU-accelerated indexing and querying, which aligns well with the Jetson’s capabilities. A practical example is deploying a product recognition system in a retail store using a Jetson Xavier NX. Here, a pre-trained model (like ResNet) generates image embeddings, which are stored locally. When a new image is captured, FAISS on the Jetson compares its embedding against the database to find matches, all without cloud dependency. Another example is using NVIDIA’s RAPIDS RAFT library, which provides GPU-optimized algorithms for approximate nearest neighbor (ANN) search. This can be integrated into Jetson-based applications for tasks like real-time sensor data analysis in industrial IoT settings, where low latency is critical.
However, there are practical considerations. First, Jetson devices have limited RAM compared to servers, so vector indexes must be optimized for memory usage. Techniques like quantization (reducing vector precision from 32-bit to 8-bit) or pruning (removing redundant vectors) can help. Second, storage constraints may require using external drives or network-attached storage for larger datasets. Third, while the Jetson’s power efficiency is a strength, developers must still balance performance with thermal limits—for instance, avoiding sustained 100% GPU usage in compact enclosures. Tools like TensorRT can optimize model inference to reduce compute load. Overall, while vector search on Jetson is feasible, it requires careful tuning to align with hardware limits, making it ideal for small-to-medium-scale applications like robotics navigation, on-device recommendation systems, or edge-based facial recognition.