Ways to Try DeepSeek R1: Hosted and Local Distilled Models

I demonstrate three ways to try DeepSeek R1: DeepSeek’s hosted chat, a distilled model hosted by Groq, and a local distilled model in LM Studio. Interfaces and model availability shown here reflect the time of the video.

In DeepSeek’s chat interface, I select DeepThink (R1); the interface also offers web search. Hosted services receive the information sent to them, so I encourage viewers to consider privacy before choosing a provider. The video does not establish how any government or third party handles a particular user’s prompts.

On Groq, I select DeepSeek-R1-Distill-Llama-70B. This is a distilled model, not the full R1. In my Tetris coding example the display reaches about 275 tokens per second. I disclose that I am a small investor in Groq.

For local inference I use LM Studio, another company in which I disclose a small investment. After installing it, open the discovery tab and search for DeepSeek. Smaller distilled versions are more practical on a personal machine than the full R1, which requires substantial hardware.

The download menu offers different quantizations. Higher-bit variants generally use more memory and may preserve more quality; I commonly choose Q4_K_M. LM Studio indicates whether the model can be offloaded to the GPU, helping you choose a file that fits your hardware.

After downloading, select the model and load it. I demonstrate the 8B Llama distill on a machine with a high-end GPU and ask it to write Snake in Python; the displayed speed is about 77 tokens per second. Your results depend on the model, quantization and hardware.

The choice is between a hosted service and local inference, with different performance and data-handling considerations. Local execution can keep prompts on the machine when configured that way, though the application and any connected features still matter. I also mention Ollama as another local option.