Five current model-catalogue candidates for running locally on a Mac mini are Qwen3.5 4B, Qwen3.5 9B, Qwen3.5 27B, Gemma 4 12B, and Gemma 4 26B. They span smaller and larger model footprints, but they are not ranked test winners: available evidence does not establish which is fastest or most accurate on the current-generation Mac mini.
Choose by the exact Mac mini’s unified-memory capacity, the tasks and modalities you need, and how much context you plan to use. A model’s download size is not its full working-memory requirement.
What “best” means for a Mac mini
These five are practical candidates, not a benchmark ranking. Ollama’s catalogue gives useful model sizes and listed context windows, but those specifications do not show how quickly or reliably each model runs on a particular Mac mini. The retrieved material contains no controlled, same-machine comparison of all five on the current generation.
For a useful comparison, consider four things together:
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- Model footprint: the published download size is a starting point, not the total memory needed while generating.
- Task and modality: catalogue listings identify text and image input for the Qwen3.5 variants below; Gemma 4 is described by its publisher for reasoning, agentic workflows, coding, and multimodal understanding.
- Memory headroom: macOS, the inference runtime, model weights, context cache, and other open apps share unified memory. A large listed context window does not mean that window will fit comfortably on every configuration.
- Apple Silicon support: Ollama and LM Studio are among the tools in Apple’s described local AI ecosystem. Ollama has described an Apple Silicon path using MLX.
Apple’s September 2026 announcement says the newly available Mac mini has M6 and M5 Pro options, but does not establish the memory configurations in the evidence available here. Check the unified-memory amount for the exact SKU before selecting a model size.
Five local model candidates
The figures below are Ollama catalogue specifications, not independent measurements. Sizes can vary by model build or quantization; the Qwen3.5 4B Q4_K_M entry and its MLX build are listed separately. Each model’s listed 256K context is a catalogue value, not a promise that a Mac mini can use the full window with acceptable memory use or responsiveness.
Rank #2
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| Model | Catalogue size | Listed context | Input or intended use | Practical fit |
|---|---|---|---|---|
| Qwen3.5 4B | About 3.3–4.0 GB for the Q4_K_M build; 4.0 GB for the MLX build (Ollama catalogue) | 256K tokens (Ollama catalogue) | Text and image input | Smaller-footprint general candidate; leave room for runtime, context, macOS, and other apps. |
| Qwen3.5 9B | About 6.6–7.6 GB (Ollama catalogue) | 256K tokens (Ollama catalogue) | Text and image input | Middle-size Qwen candidate if the chosen configuration has sufficient memory headroom. |
| Qwen3.5 27B | About 17–20 GB (Ollama catalogue) | 256K tokens (Ollama catalogue) | Text and image input | Larger candidate for a higher-memory setup; the weight footprint alone makes headroom especially important. |
| Gemma 4 12B | About 7.7–8.0 GB (Ollama catalogue) | 256K tokens (Ollama catalogue) | Publisher describes reasoning, agentic workflows, coding, and multimodal understanding as intended uses | Alternative mid-size family; stated use cases are publisher descriptions, not independent comparative results. |
| Gemma 4 26B | About 18–19 GB (Ollama catalogue) | 256K tokens (Ollama catalogue) | Publisher describes reasoning, agentic workflows, coding, and multimodal understanding as intended uses | Larger alternative for a higher-memory setup; test the actual workload and context. |
Qwen3.5 4B: the smaller-footprint candidate
Start here if preserving memory headroom matters more than choosing a larger parameter count. Ollama lists text and image input, a 256K context window, and a Q4_K_M build around 3.3–4.0 GB; its MLX build is listed at 4.0 GB. Those are catalogue file sizes, not an estimate of total memory use at a particular context length.
Qwen3.5 9B: a middle-size option
Ollama lists this text-and-image model at roughly 6.6–7.6 GB, with a listed 256K context window. It is a reasonable candidate when you want to move beyond the smallest option, provided the Mac mini has room for the model and the rest of the workload.
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Qwen3.5 27B: a larger Qwen option
At about 17–20 GB in Ollama’s listing, this is a higher-footprint choice. Its listed text and image input and 256K context may suit a workload that benefits from a larger model, but the catalogue does not establish that it will be responsive or that the full context will fit on a given mini.
Gemma 4 12B: a mid-size alternative
Ollama lists the 12B model at about 7.7–8.0 GB with a 256K context window. Gemma 4’s description names reasoning, agentic workflows, coding, and multimodal understanding as intended uses. Treat those as the publisher’s stated use cases, not proof that it outperforms a similarly sized Qwen model.
Rank #4
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Gemma 4 26B: a larger alternative
Ollama lists this model at about 18–19 GB and a 256K context window. It is a candidate for a higher-memory setup; the listing alone cannot establish usable context, speed, or task quality on a specific Mac mini.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose for your Mac mini
- Check the exact machine. Confirm the unified-memory capacity for the Mac mini configuration you own or plan to buy. Apple’s September 2026 announcement identifies M6 and M5 Pro options but does not provide their memory configurations in the evidence cited here.
- Start with the workload. For ordinary text tasks such as summarizing, extracting information, writing, or translating, test a smaller candidate first. For image input or a task that may benefit from a larger model, compare the relevant candidates using the same examples.
- Choose a context length you actually need. A 256K catalogue context is not a recommended default. Longer prompts increase memory demands; use a shorter context if it handles your work and helps preserve room for the operating system and other applications.
- Install through an Apple Silicon-compatible runner. Ollama and LM Studio are named among tools in Apple’s local Mac AI ecosystem. Ollama has described an MLX-based Apple Silicon path, but its announcement does not by itself establish the current availability or status of every model build.
- Evaluate on your own setup. Try representative prompts and check whether the model completes them within your available memory and acceptable response time. Keep the model build, quantization, runner, context, and prompt consistent when comparing options.
Apple Developer’s WWDC26 presentation describes a local AI stack comprising MLX, MLX-LM, MLX-LM Server, and an agent layer, and uses a 1.6-trillion-parameter DeepSeek model requiring more than 800 GB just for weights as an illustration of scale. That figure is not a Mac mini capacity claim; it underscores why model size and available hardware must be considered together.
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What the available figures do—and do not—tell you
Ollama’s catalogue entries are useful for narrowing candidates by variant and approximate file size. They do not give total working-memory requirements, sustained speed, or comparative accuracy for a specific Mac mini. A result measured on one configuration would not automatically apply to another: chip, unified memory, macOS, runner and version, model build and quantization, context, prompt, and measurement method all matter.
Ollama announced in 2026 that its Apple Silicon path was being previewed on MLX and described a March 29, 2026 test comparing quantized Qwen3.5-35B-A3B implementations. That vendor test is about a different model configuration and does not establish a universal speed advantage or rank the five candidates here.
Quick Recap
Sources
- Apple Newsroom: September 2026 Mac mini announcement
- Apple Developer: WWDC26 presentation
- Ollama: 2026 Apple Silicon and MLX announcement
- Ollama model catalogue: Qwen3.5
- Ollama model catalogue: Gemma 4
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