The interest in executing AI models on-premises, without relying on the cloud, has exploded following the emergence of DeepSeek. If you're concerned about privacy, speed, or offline availability, building your native AI into a Mac Mini is a great idea: your data stays at home, there are no monthly fees and the performance with Apple silicon It is surprising for a compact team.
In addition to privacy, there is another practical advantage: you can measure performance in tokens per second and check firsthand the computing capacity of your Mac. And the best thing is that the process to put DeepSeek to work is very accessible thanks to tools like LM Studio (sometimes referred to as LLM Studio) and Don't, which allow you to download, configure and converse with the model with just a few clicks or commands.
What is DeepSeek and why is it worth setting up locally?
DeepSeek has shaken up the landscape with its quality/price ratio and its open approachThere are two major flavors in play: the V3 series for web use, and the R1 branch, with a special focus on reasoning, which is the most interesting to run on your computer in versions distilledThese versions are reduced in size to run smoothly on consumer devices, while retaining much of the original's capabilities.
The proposal makes sense if you prioritize privacy and controlUnlike a cloud-based chatbot, there's no data uploading to external servers. In scenarios with irregular connectivity (train travel, restricted corporate environments, labs without a network), having a local AI that responds quickly and has no usage limits is a real advantage.
LM Studio: Dashboard for your AI on macOS
LM Studio acts as a centralized workshop to search, download and run Local language models with a simple interface. Without being a programmer, you can adjust the tone, technical level, or context length of the model, or leave everything as default and start using it in minutes.
The approach is very practical: from the discovery tab you find models, download them with one click, and load them into memory for immediate chatting. It also allows you to adjust key parameters such as load on GPU (VRAM to use), CPU threads, context length (in DeepSeek R1 distill it reaches figures like 131.072 tokens) and memory options like Keep Model in Memory o Try mmap()If you don't want to complicate things, just use the default settings and press "Load Model."
Install and use DeepSeek R1 in LM Studio step by step
The easiest way to get started on a Mac is to download LM Studio, find the right model, and load it. The app guides you through the process and, If your equipment is not suitable for a specific model, even displays alerts like “Likely too large for this machine”.
Step 1: Find the model. Open LM Studio and go to “Discover” or “Model Search.” Type “deep” in the search bar and locate “DeepSeek R1 Distill (Qwen 7B)”. In the right panel you will see the approximate size (e.g., 4,68 GB) and its features. This version is very efficient for reasoning and fits well in computers with limited memory.
Step 2: download. Click the green "Download" button (you'll see the size, for example, 4,68 GB). The side window shows you the progress, speed, and estimated time. When it's finished, the model will appear in your list of available downloads. Confirms that “DeepSeek R1 Distill Qwen 7B” appears before further.
Step 3: Adjust and load. Before clicking “Load Model”, you can configure: context length, VRAM to use, number of CPU threads, and whether to keep the model in memory. To start, the default setting will work. However, if your Mac Mini has enough unified memory, it's a good idea to enable "Keep Model in Memory” for faster refills.
Step 4: Chat. Open the “Chat” view, choose the loaded model, and type something like, “Hi, what model are you and who trained you?” If they reply with their identity and capabilities, you’ll know everything’s working. If you try a larger model (e.g., a very ambitious “DeepSeek-V3-4bit”) and it appears in red, “Likely too big for this machine”, you have to opt for a lighter variant or add RAM/VRAM on compatible computers.
Once up and running, you can use it. No internet connectionA useful trick to check this is to disable Wi-Fi in System Settings and open Activity Monitor to observe GPU usage while you chat with the model; if the graph moves, all work is done on your Mac.
Alternative: Install DeepSeek with Ollama on macOS
If you prefer the lightness of the terminal, Don't is a very popular local LLM model server. You download its app for macOS, install it like any other, and you can invoke models with a simple command.
To get started, install Ollama from its official website and run it. In Terminal, the typical command for the smaller version is: ollama run deepseek-r1:7b. If you have more memory (for example 32 GB or more) you can try higher variants (13B or, if you dare, 67B), although on Mac Mini the experience is usually more stable with 7B or 8B.
Those who want a more visual interface can connect it on top of Ollama. Some options are Chatbox AI (you point the provider to “Ollama API” and choose “DeepSeek R1 7B”) or the extension Page Assist – A Web UI for Local AI Models, which offers a ChatGPT-style panel in the browser but relying on your local AI.
To verify that everything is truly local, turn off Wi-Fi, run a query, and look again at Activity Monitor in the GPU tab. You'll see the system using the integrated graphics or Apple Silicon's unified memory. confirming that there is no traffic to the cloud.
R1 Distillate or V3? Sizes, hardware, and model selection
DeepSeek R1 in distilled versions (such as Qwen 7B or Llama 8B-based options) is the sweet spot for home equipmentIt maintains the essence of the reasoning while reducing the footprint: we're talking packages of between 4 and 8 GB, very manageable for a Mac Mini with 16 GB of unified RAM.
The top-of-the-range full version, DeepSeek‑R1:671B, is data center material. Compressed, it can be around 120 GB (with originals in the hundreds of GB) and its realistic execution requires multiple professional-grade GPUs with huge amounts of VRAM. To illustrate, there are cloud demos on nodes with eight 192GB AMD Instinct MI300X GPUs, dozens or hundreds of CPU cores, and terabytes of RAM. This isn't exactly a consumer desktop.
If you're using LM Studio, please check its compatibility notices. If you're using Ollama, please prioritize the 7B; if it runs smoothly and your usage demands it, try 13B. On Apple Silicon, power efficiency shines, and even without a dGPU, The tokens per second are very decent. for writing, brainstorming, light programming and technical consultations.
Real-world performance on Mac Mini and other Macs with Apple Silicon
Tests in a Mac mini with M4 chip and 16GB show that small/mid local models respond quickly. Although there is no dedicated graph, the unified memory and accelerators The SoC's capabilities allow for fast, low-latency text generation at common prompts.
In informal comparisons with web options such as ChatGPT (GPT‑4), Claude 3.5 Sonnet, Gemini 1.5 Flash or the online DeepSeek V3 itself, local models such as Llama 3.1‑8B, Phi‑4‑14B o DeepSeek R1‑14B They are surprising for their response speed, even when running concurrently. However, when faced with heavy loads or long prompts, The cloud continues to gain in raw muscle.
Measuring "tokens per second" locally is useful for evaluating hardware upgrades or deciding whether to upgrade to a larger model size. With LM Studio and Ollama, it's easy to repeat the same prompt and record the performance to compare configurations (more CPU threads, VRAM variations, context length, etc.).
What they get right (and what they don't): reasoning, facts, and biases
“Reasoning” tests such as counting letters offer curious results. With the word “Strawberry”, some local models fail or rush, while a DeepSeek R1 distillation can invest more time thinking but get it right, explaining step by step how to add the “r”.
In the Spanish phrase "Saint Roque's dog has no tail because Ramón Ramírez stole it," the situation becomes more complicated: several web chatbots make mistakes at the beginning and, after being asked again, correct themselves. Locally, R1 and other models They can get confused by the language or with the objective of the search (confusing “r” with “e”), which makes it clear that it is advisable to guide them and, if necessary, try again with more precise instructions.
With lateral thinking puzzles, like the one about the billiard balls 7-9-11-13 adding up to 30 if you flip the 9 to make it a 6, the uncluttered answer is usually "you can't." Even if you insist on "there's a trick," Many locals do not find the creative way, while some web services, on the trail, do resolve it.
On factual questions (e.g., table of World Cup winners and runners-up), cloud services nailed it in a recent bracket, while on-premise there was hallucinations and incorrect data (invented finalists, wrongly assigned titles, etc.). Here the recommendation is to contrast and, if you need historical accuracy, rely on verified sources or use a larger/updated model.
When addressing sensitive topics (Tiananmen, Taiwan, criticism of leaders), nuances are appreciated: some web models restrict content and DeepSeek R1 locally can reply with filters or shortcuts depending on the prompt, sometimes with messages in other languages during their "thinking." The positive side of things is that, in general, locals are restrained and respectful, and they avoid dangerous instructions (such as making a bomb), refusing with reasonable warnings.
Privacy and Local Experience: What You Need to Know
The big argument for mounting DeepSeek on your Mac is that There are no third parties reading your chatsYou don't rely on quotas or usage limits, and you can continue working without coverage. However, if you browse modeling websites or forums, you'll see cookie notices (like those on Reddit) asking for your consent; this only affects their platform. no to your local execution.
On-premises, everything is under your control: you can save conversations, adjust parameters, and decide when to update or change models. Plus, tools like AnythingLLM o Anywhere LLM They offer alternative flows with local servers and, where appropriate, web interfaces. similar to those of online chatbots but without taking your data out.
Setup Tips: Get More Out of Your Mac
If you experience slowness, lower the model size or use variants. 4-bit quantized when available. In LM Studio, if you encounter “Likely too large for this machine,” don’t force it: a stable session with well-tuned 7B/8B yields more than 13B at the limit.
Activate "Keep Model in Memory” to quickly switch between chats and brands “Try mmap()” if the platform supports it; on Apple Silicon it helps with memory management. Set the number of CPU threads to half or three-quarters of your cores so as not to saturate other tasks, and assign the VRAM conservatively if you work with heavy apps in parallel (video editing, IDEs, etc.).
The length of context is tempting (e.g., 131.072 tokens), but you don't always need that much. Reducing it improves latency and power consumption. Reserve huge contexts for long documents or code analysis, and use summary prompts on a daily basis.
If you are experimenting with multiple models at once, avoid running them in parallel on 16GB machines; alternate sessions or close the one you don't use to return unified memory to the system. Check Activity Monitor: If you see high memory pressure, it's time to unload the model, free up resources, or follow a Guide for when your Mac is locked.
Recommended workflows
If you want a ChatGPT-style interface on top of Ollama, use Chatbox AI or Page Assist. Configure “Ollama API,” choose “DeepSeek R1 7B,” and that’s it: you’ll have a clean window where Test prompts, save sessions, and switch models without typing commands.
quick questions
- Can I use DeepSeek without Internet? Yes. Download the model and, once loaded, you can turn off Wi-Fi. The calculation is done on your Mac, and the data isn't output.
- Which Mac do I need? With Apple Silicon (M-series) and 16GB of unified memory, 7GB/8GB works very well. You can try 8GB, but you'll have less headroom.
- Is it as accurate as the cloud? It depends on the case. It performs very well for reasoning and creativity; for critical factual data, verify sources or use larger models.
- Can I change models easily? Yes. LM Studio and Ollama allow you to download and switch between models (Llama, Phi, Qwen, etc.) to compare performance and style.
Mounting DeepSeek on your Mac Mini is a handy way to bring AI to your desktop with speed, privacy and total controlWith LM Studio you have a guided experience, and with Ollama you have a lightweight flow per terminal; both give you access to very capable distilled versions, convincing reasoning in many tasks, and enough power to write, program, and experiment, knowing that all processing happens on your computer.
