DeepSeek-R1

Updated
03.07.2026
Thinking
Reasoning
Code
Multilingual

Run DeepSeek-R1, a 671B reasoning model, locally with Atomic Chat. Private, offline chain-of-thought with no cloud and no limits. Free.

huggingface-cli download deepseek-ai/DeepSeek-R1
from transformers import AutoModel
model = AutoModel.from_pretrained("deepseek-ai/DeepSeek-R1")

More models

NameSize / UsageContextInput
DeepSeek-V4-Pro
1MText
DeepSeek-V4-Flash
1MText
DeepSeek-R1-0528
Reasoning, math, coding128KText
DeepSeek-V3-0324
General chat, coding, agents128KText
DeepSeek-Coder-V2-Lite-Instruct
Code generation, completion128KText
DeepSeek-V3.2
128KText

At a glance

  • License: Mit
  • Context length: 128K tokens
  • Languages: Multilingual
  • Minimum hardware: ~380 GB VRAM
  • Strengths: frontier-level chain-of-thought reasoning

Overview

DeepSeek-R1 is a reasoning model from the Chinese AI lab deepseek-ai.

  • License: Mit
  • Context length: 128K tokens
  • Languages: Multilingual
  • Minimum hardware: ~380 GB VRAM
  • Strengths: frontier-level chain-of-thought reasoning

Highlights

  • RL-trained reasoning — R1-Zero learns via large-scale reinforcement learning without prior SFT.
  • Cold-start refinement — R1 adds cold-start data before RL to improve readability and performance.
  • Frontier-level chain-of-thought — strong performance on math, code, and reasoning tasks.
  • Open distillations — smaller distilled variants (1.5B-70B) released for local use.

What it is good at

DeepSeek-R1 is built around explicit reasoning, and its open weights make it useful across a few distinct jobs:

  • Step-by-step reasoning — the model writes out a visible thinking trace before answering, which helps on logic puzzles, multi-step word problems, and questions where the path to the answer matters as much as the answer.
  • Code and debugging — it handles code generation, refactoring, and walking through why a function breaks, with the same deliberate reasoning applied to the logic of a program.
  • Multilingual work — it reads and responds across many languages, so translation, cross-language summaries, and non-English drafting all stay on-device.

Running it locally

The full DeepSeek-R1 is a 684.5B-parameter MoE model with a 128K context window, so the complete weights need a multi-GPU or server-class setup rather than a single consumer card. The smaller distilled variants (1.5B through 70B) are what most people run at home — a 7B distill fits in roughly 8 GB of VRAM, while the 70B needs around 36 GB. Grab the weights from Hugging Face:

huggingface-cli download deepseek-ai/DeepSeek-R1

From there you can serve it with vLLM or load it through Transformers, or skip the setup entirely and open it in Atomic Chat with one click.

License

DeepSeek-R1 is released under the MIT license. You can use it commercially, modify it, fine-tune it, and redistribute it, including in closed-source products, as long as you keep the copyright notice. That permissiveness is part of why so many third-party tools and distilled versions exist.

Desktop
macOS
(M1 or better)
Download
Windows
(x64)
Download
Linux
(x86_64)
Download

Frequently asked questions

DeepSeek-R1 is an open-weight reasoning model from deepseek-ai, built on a Mixture-of-Experts architecture totaling 684.5B parameters with a 128K-token context window. It was trained with reinforcement learning to produce a step-by-step thinking trace before answering. On math, code, and reasoning benchmarks it performs in the same range as OpenAI's o1.

The full 684.5B MoE model is data-center scale and needs a multi-GPU or server setup, so most people run the distilled versions instead. A 7B distill fits in about 8 GB of VRAM, a 14B needs roughly 8.5 GB, a 32B around 17.5 GB, and the 70B distill about 36 GB. Leave extra headroom, since peak VRAM during long reasoning prompts can run well above the base weight size.

Yes. DeepSeek-R1 is released under the MIT license, so the weights are free to download and run. When you run it locally through Atomic Chat there are no subscription fees and no per-token charges, and the MIT terms also allow commercial use and modification.

Yes. Once you have downloaded the weights, DeepSeek-R1 runs entirely on your own machine with no internet connection required. This keeps your prompts and data on-device, which suits private documents, secure environments, or just working without a network.

Atomic Chat lets you load DeepSeek-R1 with one click, which handles the download and setup for you. If you prefer to do it manually, pull the weights with huggingface-cli download deepseek-ai/DeepSeek-R1 and serve them through vLLM or Transformers. For most desktops, picking a distilled variant that fits your VRAM is the simpler route.