Testing DeepSeek R1 locally with Ollama

Testing DeepSeek R1 locally with Ollama has been an intriguing experience, offering a glimpse into the world of open-source AI models. As someone who values privacy and the ability to work offline, I was excited to explore this alternative to cloud-based services.

The setup process was surprisingly straightforward, thanks to Ollama’s user-friendly interface. I opted for the 7B parameter model, striking a balance between performance and my hardware limitations. While not as powerful as its larger counterparts, it proved adequate for many tasks I threw at it.

In my testing, DeepSeek R1 showed impressive capabilities in handling coding tasks and technical explanations. I found it particularly useful for brainstorming sessions and generating initial drafts for creative writing projects. The model’s ability to understand context and provide relevant responses was noteworthy, though not always consistent.

However, the experience wasn’t without its challenges. The most noticeable drawback was the speed – responses were significantly slower compared to cloud-based alternatives I’ve used. This became particularly apparent during longer conversations or when dealing with complex queries. Additionally, I encountered some limitations when trying to integrate DeepSeek with Browser Use, as the model lacked compatibility with certain tool-calling features.

Despite these hurdles, I found value in having a capable AI assistant running locally on my machine. The peace of mind that comes with knowing my data isn’t being processed on remote servers is significant. While DeepSeek R1 may not yet be a complete replacement for more advanced cloud services, it represents a promising step forward in the realm of accessible, open-source AI tools.