Deploying DeepSeek-R1 Distilled LLMs on AWS SageMaker AI

Open foundation models (FMs) are revolutionizing generative AI, empowering organizations to build and customize AI applications while controlling costs and deployments. DeepSeek AI’s DeepSeek-R1 models are a prime example, offering powerful language capabilities.

DeepSeek AI has also created distilled versions based on Llama and Qwen architectures. In DeepSeek-R1 model includes 671B parameters, 37B Activated Parameters and 128 Context length. This ensures that it can be integrated with wide range of tasks. Distillation involves training smaller models to mimic the behavior of the larger model, effectively transferring knowledge and capabilities. Smaller models like the DeepSeek-R1-Distill-Llama-8B can process requests much faster and consume fewer resources, making them ideal for production deployments where speed and cost are critical.

Why DeepSeek-R1-Distill-Llama-8B?

DeepSeek-R1-Distill-Llama-8B is part of the DeepSeek AI family, offering powerful reasoning capabilities while maintaining a smaller model size. Some key highlights include:

• Optimized reasoning through reinforcement learning (RL) without relying on supervised fine-tuning (SFT).
• Efficient deployment with lower computational costs compared to larger models.
• Self-verification and reflection abilities for solving complex problems.
• Open-source availability, making it accessible to researchers and developers.

By leveraging AWS SageMaker, users can seamlessly deploy, fine-tune, and serve this model at scale. This blog provides a step-by-step guide on integrating DeepSeek-R1-Distill-Llama-8B with AWS SageMaker AI. For deploying DeepSeek-R1 Distilled LLMs on Aws SageMaker AI with custom deployment is as follows

Steps to be followed:

1. Launch sagemaker studio

2. Create Jupyter Space and open Jupyter Lab environment.

3. Import all the libraries required:

4. Define the dictionary to configure the DeepSeek-R1-Distill-Llama-8B with AWS SageMaker.

5. Create the HuggingFace Model

6. Deploy the HuggingFace Model. To deploy DeepSeek-R1-Distill-Llama model requires GPU-powered machine.

7. Once the model is deployed, then it can be used for the prediction.

Conclusion

Deploying DeepSeek-R1-Distill-Llama-8B on AWS SageMaker provides a scalable solution for leveraging AI-powered reasoning at a lower computational cost. Through AWS SageMaker AI custom deployment, AWS infrastructure ensures efficient model serving with enhanced security and flexibility.

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