Mac mini 2026: 5 Local LLMs Ranked by Memory and Usage

With the right tools, a Mac mini is sufficient to run open models for reasoning, coding, or multimodal tasks. This guide brings together five LLMs available locally, their sizes (ranging from 14 to 43 GB in quantized form depending on the case), their context windows up to 256K, recommended memory tiers, and the corresponding Ollama commands.

Memory First: Tiers from 16 to 64 GB Depending on the Models

Requirements vary significantly depending on the model and its quantization. A quantized version of Llama 3.3 70B is noted to be around 43 GB with a context window of 128K. It is classified for Mac minis with 48 GB to 64 GB, while 16 GB and 24 GB are not expected to be comfortable. On a 64 GB Mac mini M5 Pro, a quantized 70B is presented as a legitimate local option, executable via “ollama run llama3.3:70b.” The selection is reasoned by memory tiers, which serve as starting points rather than strict limits. Quantization, context length, KV cache, execution overhead, and other ongoing tasks influence usability comfort: a model that fits in memory can still be cumbersome if it lacks headroom. At the other end, with 16 GB, gpt-oss-20b or smaller variants of Gemma 4 are cited as viable options, while Llama 3.3 70B clearly targets 48–64 GB configurations.

Two Paths for Local Execution: Ollama in CLI, LM Studio in GUI

Two solutions stand out for running LLMs on Mac mini. Ollama is presented as the simplest option for developers: install, download a model, and launch it from the terminal, for example, “ollama run gpt-oss:20b.” The platform offers local packages for gpt-oss, Gemma 4, Qwen3-Coder, and others, and exposes a local API to connect applications or coding agents. LM Studio targets users who prefer a graphical interface: it provides the ability to search for models, import them, interact with them, and make them accessible via a local OpenAI-compatible API. On Apple Silicon, it offers compatibility with llama.cpp and Apple’s MLX inference engines. To explore local AI on Mac mini, it is presented as a simple entry point.

Open Reasoning on 16 GB: gpt-oss-20b and Its Local API

OpenAI has made gpt-oss-20b and gpt-oss-120b available as open-weight reasoning models designed to operate on user-controlled infrastructure. gpt-oss-20b requires about 16 GB of memory and is advertised as suitable for Macs with 16 GB or more. This model is intended for reasoning and agent tasks, with adjustable reasoning intensity, and is offered under the Apache 2.0 license, in compliance with the gpt-oss usage policy. This model can be used via Ollama by launching “ollama run gpt-oss:20b.” According to Ollama, it weighs about 14 GB and offers a context window of 128K, making it an attractive option for a 16 GB Mac mini. The associated memory recommendation remains 16 GB or more.

Coding Locally: Qwen3-Coder and Qwen3.6 for Repositories and Agents

For software engineering, Qwen3-Coder 30B combines a total of 30 billion parameters, of which 3.3 billion are activated, with a native window of 256K. It is described as trained for agentic engineering, understanding large repositories, and long-haul coding tasks. Ollama places this model at around 19 GB locally, executable via “ollama run qwen3-coder:30b,” with a recommendation of 24 GB or more of memory. Qwen3.6, focused on agentic coding and repository-level reasoning, comes in 35B and 27B versions: the 35B via Ollama is around 23 GB with 256K context and text-image multimodality, also available in an MLX version for Apple Silicon; the 27B is noted to be about 18 GB. The associated recommendations range from 24 GB or more for the 27B to 32 GB or more for the 35B. The 35B is presented as the highest-capacity option. Execution is done via “ollama run qwen3.6:35b.”

Controlled Size Multimodal: Gemma 4 26B A4B in MoE

Gemma 4, the latest generation of open models from Google, comes in several sizes. The 26B A4B variant utilizes a Mixture-of-Experts architecture: approximately 25.2 billion parameters in total with 3.8 billion activated at inference. It processes text and images and offers a context window of 256K. An MoE model of 26B does not have the same computational requirements as a dense 26B, as only a fraction of the parameters is activated per token. Ollama directly distributes variants of Gemma 4, including the 26B, smaller versions, and a dense 31B model. Launching is done via “ollama run gemma4:26b,” with a recommendation of 24 GB or more of memory on Mac mini.

Beyond the Technical Specs: Towards a Mac mini Local AI Box

The range of available models starts with gpt-oss-20b and the more compact variants of Gemma 4, then includes gpt-oss-20b, Gemma 4 26B A4B, and Qwen3.6 27B, followed by Qwen3.6 35B, Qwen3-Coder 30B, and Gemma 4 26B, up to Llama 3.3 70B and, beyond that, more substantial local loads. These axes are presented as practical starting points, not as limits. The Mac mini is portrayed as a now-capable local AI solution, particularly in its M6 version. It is no longer essential to own a GPU station to test demanding open models: a well-equipped Mac mini can run reasoning models, coding agents, and local APIs autonomously. The focus now shifts to the size and power of the LLM one wishes to execute.