Low-Latency Vector Search at the Edge: Optimizing Local Storage for Mobile SLM Deployment
Table of Contents Introduction Why Vector Search Matters for Mobile SLMs Fundamentals of Vector Search 3.1 Exact vs. Approximate Search 3.2 Distance Metrics Challenges of Edge Deployment 4.1 Compute Constraints 4.2 Memory & Storage Limits 4.3 Power & Latency Budgets Designing a Low‑Latency Vector Index for Mobile 5.1 Choosing the Right Index Structure 5.2 Quantization Techniques 5.3 Hybrid On‑Device/Hybrid Storage Practical Implementation Walk‑through 6.1 Preparing the Embeddings 6.2 Building a TinyFaiss Index 6.3 Persisting the Index Efficiently 6.4 Integrating with a Mobile SLM 6.5 Measuring Latency & Throughput Advanced Optimizations 7.1 Cache‑Friendly Layouts 7.2 SIMD & NEON Vectorization 7.3 Dynamic Index Pruning Real‑World Use Cases 8.1 On‑Device Personal Assistants 8.2 Augmented Reality Content Retrieval 8.3 Offline Document Search in Field Devices Conclusion Resources Introduction The past few years have seen a rapid democratization of small language models (SLMs)—compact transformer‑based models that can run on smartphones, wearables, and other edge devices. While the inference side of these models has been heavily optimized, a less‑discussed but equally critical component is vector search: the ability to retrieve the most relevant embedding vectors (e.g., passages, code snippets, or product items) in sub‑millisecond latency. ...