Deploying DeepSeek-R1: A Guide to a Serverless, High-Performaning OpenAI-Compatible Endpoint

DeepSeek, a Chinese AI startup, has launched its first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1, with notable advancements in reasoning performance. DeepSeek-R1-Zero was initially trained using large-scale reinforcement learning (RL) without supervised fine-tuning, exhibiting excellent reasoning capabilities but facing issues such as repetition and poor readability. To improve performance, DeepSeek-R1 introduced cold-start data before RL, equaling the performance of OpenAI-o1 in tasks involving math, code, and reasoning. The company has open-sourced both models and six dense models distilled from DeepSeek-R1, with DeepSeek-R1-Distill-Qwen-32B setting new benchmarks for dense models. A tutorial details deploying DeepSeek on Cerebrium's serverless architecture, highlighting cost efficiencies, security, ease of deployment, and scalability. Cerebrium's architecture simplifies deploying AI models, providing a scalable, OpenAI-compatible endpoint using vLLM, with the setup process involving account creation, project initialization, and configuration using specific hardware and software requirements.