No related articles were provided; this overview synthesizes the tool descriptions below and prevailing industry trends as of 2025‑12‑01. AI for Telecom & 5G Optimization (AI‑RAN) refers to applying machine learning — including compact LLMs and other neural models — across the radio access network to improve scheduling, beamforming, handover decisions, load balancing, energy management and predictive maintenance. The central engineering tradeoffs are latency, model size, and compute placement: small, quantized models or distilled LLMs can run on-device or at the edge (reducing RTT and enabling near‑real‑time RAN decisions), while larger models typically remain in cloud or central data centers and require GPU acceleration and careful partitioning for acceptable responsiveness.\n\nOperational integration matters: Model Context Protocol (MCP) servers and cloud platform connectors let AI agents access telemetry, state and persistent memory while preserving context and auditability. Relevant tools include Cloudflare (edge Workers and MCP servers for low‑latency compute and context bridging), Pinecone (vector search for semantic state and historical context), Confluent (Kafka streaming integration for continuous telemetry), mcp‑memory‑service (hybrid fast local reads with cloud sync), Grafbase (GraphQL exposure with MCP support) and Neon (serverless Postgres via MCP). Together these components enable closed‑loop automation — streaming telemetry into vector or SQL stores, letting an on‑device or edge model act, and persisting decisions and context centrally.\n\nAs of late 2025, deployments favor hybrid architectures: lightweight on‑device inference for fast control loops paired with cloud/cluster GPUs for heavier analytics and model retraining. Key considerations are model quantization, accelerator availability at edge sites, deterministic latency budgets for control plane actions, and robust MCP‑based integration to tie inference into existing OSS/BSS and telemetry pipelines.