DeepSeek-V3
A very large open-source language model from DeepSeek, released with weights for download.
- Stars
- 105k
- Forks
- 16.7k
- Licence
- MIT
- Language
- Python
- Last update
- 1.2 years ago
- Type
- AI model / agent
No commits in over a year — check for forks or alternatives before adopting.
What it is
DeepSeek-V3 is a large language model, a text-generating AI system, with 671B total parameters of which 37B are activated for each token, using a Mixture-of-Experts design. The README says its weights are published on Hugging Face and that it can be run locally, and it also links to a chat website and API platform. The repository itself is a model card with benchmark results rather than a finished app.
- Read the architecture and training notes in the README to understand Mixture-of-Experts and multi-token prediction
- Download the weights from Hugging Face for local experimentation (hardware permitting)
- Use the hosted chat website or API platform linked in the README without running the model yourself
- Compare benchmark results against other models such as Qwen2.5 72B and LLaMA3.1 405B as listed in the README
Great for
- Researchers comparing open-weight large language models
- Developers who want to study or self-host a very large model with suitable hardware
- Teams evaluating open-source options against closed commercial models on the README's listed benchmarks
Not ideal for
- People who just want to chat with an AI: the README points to a hosted chat site instead, so you don't need this repo for that
- Anyone without access to large GPU infrastructure, since the model is 671B parameters and the README notes the Hugging Face download totals 685B
- Users who need a fully permissive license for any use, since the model weights fall under a separate Model Agreement rather than MIT
How to get it
- 1
See the README
Clone the repository
git clone https://github.com/deepseek-ai/DeepSeek-V3 - 2
Run it
- Read the README sections 3 and 6 to choose a model and running method
- Download the weights from the Hugging Face link in the README
- Follow the Section 6 'How to Run Locally' guidance, which the truncated excerpt does not show in full
- Alternatively, use the chat website or API platform linked in the README
- 3
Get the most out of it
- Read README_WEIGHTS.md for details on the main model weights and MTP modules
- Check the paper linked in the README for training and architecture details
- Note that the README mentions MTP can be used for speculative decoding to speed up inference
- Watch the README's note that MTP support is still under active community development
Downloads
Strengths
- Publishes a detailed technical summary and benchmark tables openly
- Offers both base and chat-tuned model variants with a 128K context length listed
- Code is under the MIT license, and the README links the paper for deeper detail
Trade-offs
- Requires very large hardware to run locally; the README does not give exact requirements in the excerpt
- Model weights use a separate Model Agreement license, so MIT does not cover them
- Benchmarks are self-reported by the authors in the README and I could not independently verify them
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.