Deploy and fine-tune DeepSeek-R1 models on AWS using Hugging Face Inference Endpoints, Bedrock, and SageMaker

AI Impact Summary

Guidance to deploy and fine-tune DeepSeek-R1 models on AWS using multiple deployment paths: Hugging Face Inference Endpoints, Amazon Bedrock, and SageMaker (including Jumpstart). It includes concrete model variants (DeepSeek-R1-Distill-Llama-70B), hardware mappings (ml.g6.48xlarge and ml.inf2.48xlarge), and sample Python/SageMaker SDK steps. Notable notes include a per-endpoint cost of about $8.3/hour, prerequisites such as a SageMaker Domain and quota adjustments, and ongoing work to enable Inferentia/Neuron-based deployments. This enables teams to deploy large reasoning models in production with managed infrastructure but requires careful cost governance and lifecycle management.