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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 runn...
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