DeepSeek FAQ – Updated

Welcome back! It has been three weeks since the release of DeepSeek R1, and we’re glad to see how this model has been helpful to many users. At the same time, we have noticed that due to limited resources, both the official DeepSeek website and API have frequently displayed the message "Server busy, please try again later." In this FAQ, I will address the most common questions from the community over the past few weeks.

Q: Why do the official website and app keep showing 'Server busy,' and why is the API often unresponsive?

A: The official statement is as follows:
"Due to current server resource constraints, we have temporarily suspended API service recharges to prevent any potential impact on your operations. Existing balances can still be used for calls. We appreciate your understanding!"

Q: Are there any alternative websites where I can use the DeepSeek R1 model?

A: Yes! Since DeepSeek has open-sourced the model under the MIT license, several third-party providers offer inference services for it. These include, but are not limited to: Togather AI, OpenRouter, Perplexity, Azure, AWS, and GLHF.chat. (Please note that this is not a commercial endorsement.) Before using any of these platforms, please review their privacy policies and Terms of Service (TOS).

Important Notice:

Third-party provider models may produce significantly different outputs compared to official models due to model quantization and various parameter settings (such as temperature, top_k, top_p). Please evaluate the outputs carefully. Additionally, third-party pricing differs from official websites, so please check the costs before use.

Q: I've seen many people in the community saying they can locally deploy the Deepseek-R1 model using llama.cpp/ollama/lm-studio. What's the difference between these and the official R1 model?

A: Excellent question! This is a common misconception about the R1 series models. Let me clarify:

The R1 model deployed on the official platform can be considered the "complete version." It uses MLA and MoE (Mixture of Experts) architecture, with a massive 671B parameters, activating 37B parameters during inference. It has also been trained using the GRPO reinforcement learning algorithm.

In contrast, the locally deployable models promoted by various media outlets and YouTube channels are actually Llama and Qwen models that have been fine-tuned through distillation from the complete R1 model. These models have much smaller parameter counts, ranging from 1.5B to 70B, and haven't undergone training with reinforcement learning algorithms like GRPO.

If you're interested in more technical details, you can find them in the research paper.

I hope this FAQ has been helpful to you. If you have any more questions about Deepseek or related topics, feel free to ask in the comments section. We can discuss them together as a community - I'm happy to help!