// 资讯详情
在 Guides & References / AI 下添加"本地运行 LLM"子章节：本地部署的原因与时机、硬件配置（VRAM、KV 缓存、量化）、Ollama、llama.cpp、vLLM / SGLang、桌面端运行器（LM Studio、Jan、Docker Model Runner、LocalAI、MLX-LM）、NVIDIA NIM、开源权重模型系列与许可、LLM 网关（LiteLLM）、推理优化，以及将本地模型与 GitHub Copilot（BYOK）、Claude Code（LLM 网关 / Anthropic 兼容端点）及其他编程助手集成，包含 MathJax、参考文献与一页速查表 PDF
Add "Running LLMs Locally" sub-section under Guides & References / AI: why and when to run on premise, hardware sizing (VRAM, KV cache, quantization), Ollama, llama.cpp, vLLM / SGLang, desktop runners (LM Studio, Jan, Docker Model Runner, LocalAI, MLX-LM), NVIDIA NIM, open-weight model families and licences, LLM gateways (LiteLLM), inference optimisation, and integrating local models with GitHub Copilot (BYOK), Claude Code (LLM gateway / Anthropic-compatible endpoints) and other coding assistants, with MathJax, bibliography and a one-page cheat sheet PDF
Add "Running LLMs Locally" sub-section under Guides & References / AI: why and when to run on premise, hardware sizing (VRAM, KV cache, quantization), Ollama, llama.cpp, vLLM / SGLang, desktop runners (LM Studio, Jan, Docker Model Runner, LocalAI, MLX-LM), NVIDIA NIM, open-weight model families and licences, LLM gateways (LiteLLM), inference optimisation, and integrating local models with GitHub Copilot (BYOK), Claude Code (LLM gateway / Anthropic-compatible endpoints) and other coding assistants, with MathJax, bibliography and a one-page cheat sheet PDF