DeepSeek & open models
that run on your machine
Until recently, powerful AI lived only in the clouds of OpenAI, Anthropic and Google. DeepSeek and other open models changed the equation: GPT-level models — free, open, and running on your own machine. In this guide: what makes DeepSeek special, how to run a model locally with Ollama, the open-model map, and when it beats the cloud API.
What DeepSeek is and why everyone talked about it
DeepSeek is a Chinese AI lab that released a family of open models that shook the industry. Two models stood out: DeepSeek-V3 — a strong general chat model, andDeepSeek-R1 — a reasoning reasoning model that thinks before it answers, at the level of the closed competitors, but at a fraction of the cost and with open weights for everyone.
The news was not just the performance, but the license and price: you can download the model, run it on your own server, fine-tune it, and build a product on it — without paying any vendor and without sending data out. This is what made 2026 the year "open model" became a serious business option, not just an experiment.
DeepSeek uses a Mixture-of-Experts (MoE) architecture — only a small part of the parameters is active per query, which dramatically lowers the inference cost. And R1 was trained withReinforcement Learning to learn to "think out loud" (chain-of-thought) — which is why it is strong at math, code and logic.
Why an open model at all — 4 advantages
Running locally — Ollama and LM Studio
The good news: you do not need to be an ML engineer to run an open model. Two tools made it as simple as installing an app:
Ollama — the fast CLI way
Ollama is the most popular tool for running models locally. One command downloads and runs a model, and exposes an OpenAI-compatible API you can connect any app to:
# install (Mac/Linux) — one line
curl -fsSL https://ollama.com/install.sh | sh
# download and run a model — one command
ollama run deepseek-r1:7b
# now there is a local OpenAI-compatible API at:
# http://localhost:11434/v1LM Studio — a GUI for those who prefer it
LM Studio is a desktop app with a graphical interface: you search a catalog for a model, download it with a click, and chat with it — or start a local API server. Perfect for those who dislike the terminal, and for quickly trying several models.
Open models come incompressed versions (quantized) — the GGUF format with different precision levels (Q4, Q5, Q8). Q4 saves memory dramatically with minimal quality loss. This is what lets you run a 7B–14B model on a regular laptop. Rule of thumb: start with Q4_K_M.
The 2026 open-model map
DeepSeek is not alone. Here are the leading open-model families and what each is good at:
| Family | By | Strength |
|---|---|---|
| DeepSeek (V3 / R1) | DeepSeek | Reasoning, code, cost |
| Qwen | Alibaba | Code, multilingual, general |
| Llama | Meta | Most common in enterprises |
| Gemma | Small and efficient, on-device | |
| Mistral | Mistral AI | European, multilingual |
You can find them all onHugging Face and run them through Ollama. Note the size: a number like 7b or 70b is the number of parameters (in billions) — the larger it is, the smarter, but the more memory it needs.
How much hardware you really need
| Model size | Memory (RAM/VRAM) | Suits |
|---|---|---|
| 1B–3B | 4–8GB | A laptop, simple tasks |
| 7B–8B | 8–16GB | Mac M-series, everyday use |
| 14B–32B | 16–32GB | A workstation, a strong GPU |
| 70B+ | 48GB+ | A server / cloud GPU |
No suitable hardware? You can run the same open models through cloud inference providers (Together, Groq, Fireworks, OpenRouter) — pay per use, without managing a server, and still enjoy the flexibility of the open model.
Open vs closed — when to choose which
| Need | Open model (DeepSeek/Qwen) | Closed API (GPT/Claude) |
|---|---|---|
| Data privacy | ✓ wins | depends on vendor |
| Maximum quality | very close | ✓ still leads |
| Cost at high volume | ✓ cheaper | gets expensive |
| Setup speed | requires setup | ✓ instant |
"Open" does not always mean "allowed for any commercial use". Each family has its own license (Apache 2.0, the Llama license, and more) — read it before you build a product. And if you use DeepSeek's cloud API (as opposed to running locally), note that the data goes to servers in China; for local runs this is not an issue.