Edge & Mobile AI Inference Platforms cover the hardware, runtimes, and software stacks that move inference off the cloud and into phones, laptops, edge cameras, and local clusters. This topic examines the convergence of AI-enabled processors (e.g., Intel Core Ultra / “Panther Lake” family), optimized mobile AI runtimes (Android NNAPI, Core ML and vendor SDKs), and emerging orchestration and data layers that enable low-latency, private, and cost‑efficient deployments. Relevance and timing: mobile NPUs, heterogeneous CPU/GPU/NPU designs, and improved model quantization/compilation toolchains have made practical on-device LLM and multimodal inference feasible by 2026. That shift reduces bandwidth and privacy risk, drives new decentralized inference patterns, and changes how teams manage data, models, and governance at the edge. Key tools and roles: Mistral AI and Cohere provide enterprise-focused foundation models and private deployment options that can be adapted for edge/offline use; Stable Code supplies compact, edge‑ready code LLMs for local developer tools; Activeloop’s Deep Lake functions as a multimodal AI data platform for versioning, streaming, and RAG-ready vectors at the edge; Warp exemplifies developer-facing IDE/agent tooling that integrates local model runtimes; and Vertex AI represents cloud-managed pipelines for training, model optimization, and hybrid deployments. Practically, production edge stacks combine model selection and quantization, runtime targeting (mobile NN runtimes and vendor SDKs), data/version control (Deep Lake), and orchestration across devices and cloud (Vertex-like services). The result is a pragmatic, privacy-conscious architecture for vision, agentic, and decentralized AI workloads that balances latency, accuracy, and governance.