This topic covers open‑source large language models (LLMs) and ecosystem tools that enterprises use to run confidential, cost‑effective AI on cloud, on‑premises and at the edge. In 2025 the landscape centers on foundation models (e.g., Mistral, Llama, Falcon) and a growing set of specialist, instruction‑tuned and compact models for code, retrieval and device deployment. Practical concerns—latency, model size, quantization, inference stacks, and governance—drive model selection more than raw parameter counts. Key tools: Stable Code and StarCoder target code completion and generation (Stable Code as edge‑ready 3B models; StarCoder as a 15.5B multilingual code model trained with Fill‑in‑the‑Middle). WizardLM is an open instruction‑following family used for fine‑tuned assistants; CodeGeeX and Salesforce CodeT5 provide open research/code models and integrations for developer tooling. LangChain and LlamaIndex are platform layers for orchestration, retrieval‑augmented generation (RAG) and production pipelines that connect models to data and observability. Relevance and trends: enterprises are adopting open models to reduce vendor lock‑in, enable on‑premise privacy, and tune models to specific domains. Edge deployments favor smaller, quantized models and optimized runtimes; cloud/edge hybrids use RAG and modular stacks to keep sensitive data local while leveraging larger models for complex tasks. Security and governance now require provenance, fine‑grained access controls, and evaluation pipelines integrated with AI data platforms. Decentralized infrastructure, model hubs and toolchains for testing, quantization and monitoring are maturing, making open‑source LLMs a practical option for regulated and latency‑sensitive use cases.