Open Weights and Running DeepSeek Locally

One of DeepSeek's most important features is not a button in the app: it is that many of its models are open weight, released under the permissive MIT license. That means you can download the model and run it on your own computer or server, with no account, no per-message fee, and your data never leaving your machine. This lesson explains what open weights are, why they matter, and how running DeepSeek locally actually works.

What You'll Learn

• What "open weight" means and how it differs from open source
• Why the MIT license is a big deal for builders and privacy
• Realistic ways to run DeepSeek locally, including smaller distilled models
• The honest trade-offs of self-hosting

What "Open Weight" Means

When a lab trains a model, the result is a giant file of numbers called weights, the learned settings that make the model work. Most leading assistants keep their weights secret; you can only reach them through the company's servers. DeepSeek instead publishes the weights for many of its models, so anyone can download and use them directly.

DeepSeek releases these under the MIT license, one of the most permissive licenses in software. In plain terms, you are allowed to use the weights for free, for personal or commercial projects, modify them, build products on top of them, and even use them to help train other models. That freedom is why DeepSeek is often compared to Meta's Llama rather than to closed models like ChatGPT.

A small clarification: "open weight" is not quite the same as fully "open source." You get the trained model to use freely, but not necessarily every detail of the training data and process. For nearly all practical purposes, though, open weights are what let you run and build on the model yourself.

Why This Matters

Open weights unlock three things you cannot get from a closed model:

• Privacy and control. When a model runs on your own hardware, your prompts and data never leave your machine. For sensitive work, that sidesteps the concern (covered next lesson) that DeepSeek's cloud stores data on servers in China.
• No per-use cost. Once downloaded, the model is free to run. You pay only for your own electricity and hardware, not per token.
• No lock-in or shutdown risk. A model you have downloaded cannot be taken away, rate-limited, or discontinued out from under you. You can keep running it for years.

How Running Locally Actually Works

You do not run these models by opening a website. You use a tool that loads the weights and lets you chat with them offline. The most popular beginner-friendly option is Ollama, a free program that downloads and runs open models with a single command. Other common tools include LM Studio (a friendly desktop app) and, for servers, vLLM.

The basic idea with Ollama looks like this:

# 1. Install Ollama (Mac, Windows, or Linux)
# 2. Download and run a DeepSeek model:
ollama run deepseek-r1

That command pulls the model weights to your computer once, then lets you chat with it in your terminal, fully offline. LM Studio does the same thing through a point-and-click interface if you prefer not to use the command line.

The Reality of Hardware

Here is the honest part: the full flagship DeepSeek models are enormous and need serious, expensive hardware (many gigabytes of high-end GPU memory) that most people do not have at home. Running the top model locally is realistic for well-funded teams and servers, not a typical laptop.

The good news is distilled models. DeepSeek and the community publish smaller versions that were trained to imitate the big R1 reasoning model. These come in sizes that fit normal machines:

• A roughly 1.5-billion-parameter distill is about 1 GB and runs on almost anything.
• An 8-billion-parameter distill is around 5 GB and runs comfortably on a modern laptop with enough memory.
• Larger 32B or 70B distills need a strong desktop GPU.

The smaller distills are less capable than the full cloud model, but they are genuinely useful for learning, private note-taking, offline drafting, and simple reasoning, all with complete privacy and zero ongoing cost.

The Honest Trade-Offs

Self-hosting is powerful but not free of friction:

• Setup takes some technical comfort, though tools like Ollama and LM Studio have made it far easier than it used to be.
• Smaller local models are weaker than the flagship cloud versions. Match your expectations to the model size.
• You maintain it. Updates, storage, and performance are on you.

For most people, the free cloud app is the right daily driver, and local models are worth exploring when privacy, offline use, or freedom from per-use cost genuinely matter to you.

Key Takeaways

• Many DeepSeek models are open weight under the permissive MIT license, so you can download and run them freely.
• Running locally keeps your data on your machine, costs nothing per use, and cannot be shut off or rate-limited.
• Tools like Ollama and LM Studio make local models approachable; a single command downloads and runs one.
• Full flagship models need expensive hardware, but smaller distilled models run on normal laptops.
• Self-hosting trades some setup effort and capability for privacy, control, and zero ongoing cost.