Here at DigitalOcean, we have been carefully watching the closing of the gap between open-source Large Language Models (LLMs) and their commercial, closed-source counterparts. One of the most important capabilities of these models is reasoning - the action of thinking about something in a logical, sensible way.
For a long time, LLMs were very linear. When given a prompt, they provided an answer. There is no meta-logic involved, or any stage where the model might be able to self-correct if it is mistaken. This effectively hinders their ability to reason, question, or adjust to problems that may be inherent to the instruction they are responding to. For example, with low-reasoning models, complex language based mathematics problems may be too complicated to solve without explicit instructions and work on the users part.
Enter the latest generation of reasoning LLMs. Ushered in by OpenAI’s O1 model series, reasoning models have taken the community by storm as they have effectively closed the gap between human and machine learning capabilities on a variety of logic tasks. These include coding, mathematics, and even scientific reasoning.
Like with all previous steps forward in development, the open source community has been working hard to match the closed-source models capabilities. Recently, the first open-source models to achieve this level of abstract reasoning, the Deepseek R1 series of LLMs, was released to the public.
In the first of this 2 part article series, we will show how to run these models on DigitalOcean’s GPU Droplets using Ollama. Readers can expect to learn how to set up the GPU Droplet, install Ollama, and begin reasoning with Deepseek R1.
The first thing we need to do is set up our machine. To begin, create a new GPU Droplet following the procedure shown in the official DigitalOcean documentation.
We recommend selecting the “AI/ML Ready” OS and using a single NVIDIA H100 GPU for this project, unless you intend to use the largest, 671B parameter model.
Once your machine has started up, proceed to the next section.
For this demonstration, we will take advantage of the incredible work done by the Ollama developers to bring our model online at rapid speed. Open up a web console window using the button on the top right of your GPU Droplet details page, and navigate to the working directory of your choosing.
Once you are in the place you would like to work, paste the following command into the terminal:
This will execute the installation of Ollama onto our machine. This process may take a few minutes as it installs. Once it has completed, everything is ready to go! Wasn’t that simple?
Now, all we need to do is execute the command that runs DeepSeek R1 on our machine. Ollama provides all of the available model sizes (1.5b, 7b, 8b, 14b, 32b, 70b and 671b parameters), so we recommend using the largest available to run on a single GPU, the 70b model.
This will take a few minutes the first time it is run to download the roughly 40GB model onto our machine and load it, but from here, we will get an interactive window to chat with our open source reasoning model.
Info: Deploy DeepSeek R1, the open-source advanced reasoning model that excels at text generation, summarization, and translation tasks. As one of the most computationally efficient open-source LLMs available, you’ll get high performance while keeping infrastructure costs low with DigitalOcean’s GPU Droplets.
As we mentioned at the start of this article, R1 is capable of handling complex tasks related to mathematics, coding, and even scientific reasoning. A reasoning model is a framework or set of principles that guide how an AI system reasons, makes decisions, and solves problems. It outlines the algorithms, techniques, and heuristics that enable the AI to draw inferences, draw conclusions from available information, and generate solutions to complex tasks. It does this by using the iterative nature of GPT models with metalogic reasoning to better understand and infer solutions to the proposed problem.
Let’s try a sample math problem (source), and see how the model reasons the answer back to us:
Look closely at the way the model reasoned out the response to this question. It first determined the parameters for solving the question: finding the total weight of the flower pots and packing material. It then reiterated the facts from the problem, and showed its work reasoning the total weight of the flower pots and packing materials individually. It then combined those values, and then compared the two values to get the correct answer: no.
Now, let’s look at a coding example:
Again, this is a very telling example of the model’s reasoning skills. In the response, it first provided a sufficient answer. Continuing on, it reasoned that the answer may not consider all possible edge cases for the user, such as converting all files in a directory. To ameliorate this, it provided another working solution that better solves every potential problem. It then expounded upon these considerations in detail to conclude the response. Overall, this is a very impressive and comprehensive solution that nearly mimics the reasoning of a human actor.
Based on these responses, we recommend trying all sorts of challenging questions with R1. The model is incredibly robust, especially at the 60b param level and up.
In this article, we showed how to run DeepSeek R1 using DigitalOcean’s GPU Droplets with Ollama. As we saw above, this provides us with a fast and powerful reasoning mechanism to aid us across a variety of tasks, including programming and math. We were very impressed with these models, and will definitely be using them to facilitate projects wherever possible.
Check back soon for part two of this series, where we will dig deeper into R1’s architecture, expand on how the model training was done, and learn what makes the model’s reasoning so powerful.
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what droplet configuration would you recommend for the 671b model?
what droplet configuration would you recommend for the 671b model?
Great article! I have a question about pricing. After you create the droplet, you are only charged for the time the GPU is being used. Is this correct? So the droplet can exist, but if you are not actively using it, you are not charged? Thank you.