Local-first LLMs are moving from the cloud to on-device runtimes, slashing latency and boosting privacy. The buzz is powered by guides like Kimi K2 Thinking: How to Run Locally [1] and a wave of on-device tooling, from Ollama to LM Studio [1]. Some visuals even imagine AI chips and distributed edge compute replacing centralized cloud fears [3].
Where to start - Ollama lets you run local models like gpt-oss-20b on approachable hardware [5]. - LM Studio is a go-to backend for local LLMs, with a focus on practical workflows [2]. - Popular local options also include GLM 4.6 with Gemma 3 for smaller setups [2], and Qwen3-4B-Instruct-2507-Q6_K as a tested choice [2]. - For offline knowledge with RAG, many turn to AnythingLLM and nomic-embed together [6].
Hardware reality - A typical starter setup is a MacBook Air M4 with 16GB RAM, paired with local runtimes like Ollama or LM Studio [2]. - For bigger contexts, folks push toward a Mac Studio M3U 512GB RAM or higher, tweaking models like GLM 4.6 and Gemma 3 in terminal-friendly workflows [2][4]. - Some experiments run 128GB RAM machines with these stacks, balancing speed and memory [4].
RAG, docs, and the future - The community leans into offline RAG and document access, with AnythingLLM alongside nomic-embed guiding offline workflows [6]. A cloud-free vision persists: AI intelligence becomes private, local, and distributed [3].
Bottom line: local-first tooling is maturing fast, with open-source options and hardware-aware setups that fit on everyday devices. Watch for more Mac-friendlyruns and lighter-weight models that keep data private without slowing you down.
References
Kimi K2 Thinking: How to Run Locally
Guide on locally running Kimi K2 Thinking model; discusses setup, requirements, and deployment considerations for local execution and portability.
View sourceStarting with local LLM
User seeks to run a local LLM on Mac hardware with RAG integration; discusses models, setup steps, and recommendations online.
View sourceWhat if AI didn’t live in the cloud anymore?
Discusses on-device LLMs, private data, distributed intelligence, and industry directions toward local chips and edge AI for privacy and efficiency.
View sourceTerminal based inference on a Mac with lots of model options
Discusses Mac-local LLMs via Ollama; critiques model options, cloud reliance, seeks open-source, non-GUI workflow using llama.cpp and GGUF.
View sourceBest local ai for m5?
User seeks privacy-focused local LLM options for M5 Mac; mentions GPT-oss 20B via Ollama and LM Studio.
View sourcehow to feed my local AI tech documentation?
Discusses feeding docs to local LLMs using RAG, offline backends, and testing with Mistral 7B and AnythingLLM, plus training ideas.
View source