How to run deepseek r1 locally better than openai o1 tutorial

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running deepseek r1 locally: a comprehensive guide beyond openai's o1 tutorial

this tutorial goes beyond the basics of running deepseek r1 locally and aims to provide a more comprehensive understanding of how to optimize its performance, integrate it into your projects, and troubleshoot common issues. we'll cover installation, model loading, inference, optimization strategies, and comparing its performance with openai's models. this tutorial assumes you have a basic understanding of python and command-line operations.

**why deepseek r1 over openai's o1 (local models)?**

while openai's released local models (if you're referring to local access to openai models via api keys or through hypothetical future releases) are yet to be fully realized, deepseek r1 offers a compelling alternative due to:

* **accessibility:** deepseek r1 is openly available for download and local execution. this gives you complete control over your data and privacy, eliminating reliance on external api services.
* **fine-tuning potential:** deepseek r1, especially if you are using the base model, is designed to be fine-tuned on specific datasets, allowing for highly specialized performance.
* **cost-effectiveness:** once downloaded, running deepseek r1 locally eliminates per-token costs associated with api usage, leading to significant savings for large-scale deployments.
* **offline operation:** deepseek r1 operates without an internet connection, providing consistent access even in environments with limited or no connectivity.
* **transparency and customization:** as an open-source model (or at least available for download and local execution), you have more insight into its architecture and can modify it for specific use cases (within licensing terms).

**prerequisites:**

* **hardware:**
* **gpu:** a nvidia gpu with at least 16gb vram is highly recommended for optimal performance. 32gb or more is ideal for larger models and faster inference. while cpu inference is p ...

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