For most Mac users, the simplest free setup is Ollama: install the macOS app, launch it, then pull a small DeepSeek-R1 distilled model from Terminal. If you prefer buttons instead of commands, use LM Studio. Both can run a downloaded model locally, including offline after the model files are on your Mac.
Before you install: check these Mac requirements
- macOS: Ollama requires macOS Sonoma 14 or newer.
- Processor: Ollama supports Apple silicon (M-series) with CPU and GPU support. Intel (x86) Macs are supported with CPU only.
- Storage: language models can consume tens to hundreds of gigabytes. Check available space before downloading; an external USB-C SSD is optional if your internal drive is limited.
- Internet: you need a connection to download the app and model weights. After the weights are installed, local chat can work without an internet connection, subject to the runtime’s optional integrations.
The complete DeepSeek-R1 and R1-Zero releases contain 671B total parameters and a 128K context. Those versions are not a practical starting point for an ordinary Mac. Begin with a smaller distilled model instead; this recommendation follows the published parameter sizes, not a vendor minimum requirement.
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Option 1: install DeepSeek with Ollama (recommended)
1. Download and copy Ollama to Applications
- Download the Ollama macOS installer from its official site.
- Open the downloaded
ollama.dmg. - Drag
Ollama.appinto the system-wideApplicationsfolder. - Open Ollama from Applications and approve any macOS prompts.
Ollama describes this as its preferred installation method: “The preferred method of installation is to mount the ollama.dmg and drag-and-drop the Ollama application to the system-wide Applications folder.”
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On first launch, Ollama checks whether the ollama command is on your PATH. If it offers to create a link in /usr/local/bin, accept it. Then open Terminal and verify the installation:
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ollama --versionIf Terminal says the command cannot be found, quit and reopen Terminal after accepting the link prompt, then run the command again.
3. Download and start a small DeepSeek-R1 model
Choose a smaller distilled model for an initial test. A typical Ollama workflow is:
ollama pull deepseek-r1:7b
ollama run deepseek-r1:7bThe exact model tags available can change. If that tag is not listed by your Ollama installation, search Ollama’s current model catalog and use the published tag for a DeepSeek-R1 distilled variant. Larger 14B, 32B or 70B variants generally require substantially more memory and storage than 1.5B, 7B or 8B models.
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At the prompt, type a question and press Return. Use /bye to leave the session. Ollama stores model files and configuration under ~/.ollama and logs under ~/.ollama/logs.
4. Use the local model without a hosted subscription
Once the model has finished downloading, Ollama can answer locally. A local installation does not require a paid DeepSeek web or API subscription. Privacy still depends on the runtime, the particular model build, and any optional web-search or integration features you enable.
Option 2: install DeepSeek with LM Studio (GUI)
1. Install the app
- Download and install the LM Studio macOS app.
- Use the build appropriate for your Mac: LM Studio supports Apple Silicon and x64/ARM64 systems.
- Open the app and allow macOS to launch it.
2. Find and download DeepSeek-R1
- Open LM Studio’s model search.
- Search for DeepSeek-R1.
- Select a model size and quantization that fit your available memory and storage.
- Download the model weights inside LM Studio before trying to chat.
LM Studio supports GGUF models through llama.cpp and Apple’s MLX format on Apple silicon. After the weights are present, you can start a chat from the app and continue working offline.
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Ollama or LM Studio: which should you choose?
| Consideration | Ollama | LM Studio |
|---|---|---|
| Setup style | App installation followed by Terminal commands | Graphical installer, model search and chat controls |
| Best for | Repeatable commands, scripts, local APIs and runtime control | Beginners who want to browse, download and chat in one interface |
| Model paths | Ollama model library and compatible imports | GGUF through llama.cpp and MLX on Apple silicon |
| Intel Mac in Ollama | CPU only | Hardware behavior depends on the selected LM Studio runtime and model |
| Offline use | Available after model download; optional integrations may connect out | Available after model download; optional integrations may connect out |
| Storage | Model-dependent; large models can consume tens to hundreds of GB | Model-dependent; large models can consume tens to hundreds of GB |
Which DeepSeek model size makes sense on a Mac?
DeepSeek’s published distilled families include Qwen and Llama variants at 1.5B, 7B, 8B, 14B, 32B and 70B parameters. Smaller files usually start faster and place less pressure on unified memory; larger files can provide stronger reasoning but may be slow or fail to load on a machine with limited memory.
- 1.5B–8B: sensible first downloads for many consumer Macs and for testing the setup.
- 14B–32B: a middle ground for Macs with more memory and patience for longer generation times.
- 70B: an advanced choice requiring substantial memory and storage; confirm that your chosen runtime can load it before downloading.
- 671B full R1/R1-Zero: not a realistic local starting point for a normal Mac.
DeepSeek reports a 72.6 score for DeepSeek-R1-Distill-Qwen-32B on its listed AIME 2024 column and 1189 for DeepSeek-R1-Distill-Qwen-7B on its listed GPQA column (DeepSeek-AI, 2025). These are repository benchmark figures, not promises of speed, quality or compatibility on your Mac.
Advanced route: run a GGUF model with llama.cpp
If you want direct control over a GGUF model, the DeepSeek-R1-GGUF model card documents these macOS commands:
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curl -LsSf https://llama.app/install.sh | sh
llama serve -hf lmstudio-community/DeepSeek-R1-GGUF:Q4_K_MFor a command-line session instead of a server, use:
llama cli -hf lmstudio-community/DeepSeek-R1-GGUF:Q4_K_MThe same model card documents an Ollama-compatible route:
ollama run hf.co/lmstudio-community/DeepSeek-R1-GGUF:Q4_K_MRuntime and model-card syntax can change. Check the linked model documentation immediately before running these commands, especially if an error reports an unknown flag or model reference.
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Storage, downloads and offline troubleshooting
Not enough disk space
Stop before downloading another quantization, remove unused models from your runtime, or move model storage to an optional external USB-C SSD. Do not assume a 1TB drive is required; the appropriate capacity depends on how many models and versions you keep.
The model is too slow or will not load
- Quit memory-heavy applications and try a smaller distilled model.
- Use a lower-size quantization when several downloads are offered.
- On an Intel Mac using Ollama, expect CPU-only execution.
- Do not treat a model’s parameter count as a guaranteed memory requirement; runtime, quantization and context length also affect actual use.
Offline chat does not work
Confirm that the model download completed and that the runtime can see the local files. Disable optional web-search or connector features if you need a fully local session. A hosted DeepSeek account is separate from these local runtimes.
Licensing and hosted DeepSeek are separate choices
DeepSeek’s release page says, “DeepSeek-R1 is now MIT licensed for clear open access.” Distilled Qwen and Llama variants are open-sourced but inherit obligations from their respective base-model licenses, so check the license for the exact model before commercial redistribution.
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DeepSeek also provides a hosted web, app and API service. That service is separate from installing and running model weights locally for free.
Quick Recap
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