deepseek-v41-flash-mac-mini
by atbender
This is a recipe and experimental runner that lets a 16 GB M1 Mac mini run the full, original DeepSeek V4.1 Flash model by streaming weights from the SSD. It is a proof of concept for running very large models on small machines, at about 23 seconds per token.
- Stars
- 89 +2 a day, est.
- Licence
- Apache-2.0
- Runs on
- Your computer
- Setup
- Technical
- Language
- Python
- Age
- 45 days
What you can do with it
- Run the full DeepSeek V4.1 Flash model locally on a 16 GB Mac without a smaller or quantised substitute
- Leave long text generations running overnight on a spare Mac mini
- Reproduce and check the published benchmarks to study SSD-streaming inference
Make money with it
"
An idea, not financial advice. Check the licence before charging for anything built on it.
Ways a business could use it
- Process private text offline on hardware you already own, avoiding per-token fees and keeping data off third-party servers, for non-urgent batch jobs
- Use it as a research reference when assessing whether large open models can run on cheap local hardware
Can you use it commercially?
Free to use, change and sell, including in paid products. Keep the copyright notice.
Not legal advice. Read the project's licence file before building a product on it.
Getting it running
Clone the repository, create a Python 3.12 environment with uv, install the requirements, then download about 510 GB of model weights from Hugging Face and run the streaming script.
Follow the official instructions in the project README.
Things to check
- Very slow: about 23 seconds per token, so unsuitable for interactive use
- Needs 550–600 GB of free SSD space and a roughly 510 GB download
- Experimental and independent, not an official DeepSeek runtime
- Tested only on one machine (M1 Mac mini); external drives not benchmarked
- Heavy SSD reading and swap use may add wear and memory pressure
- Runs are not checkpointed, so a crash or reboot loses progress
Who's talking about it
Checked 27 Oct 2026. Summary drafted by AI from the project's README.
