Run DeepSeek-R1 Locally on your System using Python! 🚀

Author(s): Krishan Walia

Originally published on Towards AI.

Guide to running any LLM locally with minimum resource requirements.

This member-only story is on us. Upgrade to access all of Medium.

Not a member?Access the full article here (and don’t forget to leave at least 5 claps 👏🏻👏🏻)

The best thing about open-source LLMs is that you can run them locally on your system! 💻

The ability to run a model locally on one’s system often opens a lot of possibilities for experimenting with new projects and trying out new modifications. And now with the staggering performance metrics floated for DeepSeek-R1, who doesn’t want to run it locally?

This article has been curated to teach you how to run an LLM locally on your system, as soon as doesn’t fall outside your system’s capabilities.

Stick till the end, and you will be having a proper structure to download and run any LLM on your system!

DeepSeek R1 has introduced a completely new way by which LLMs are trained and has brought an impressive change in the way these models respond after thinking and performing a set of reasoning.

This major change in performing the thinking and reasoning before responding has brought really remarkable results in most of the important metrics. And that’s why DeepSeek R1 has become the go-to choice for almost all savvy developers… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI