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1 DeepSeek R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled versions of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes reinforcement discovering to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying feature is its reinforcement learning (RL) action, which was used to refine the design's responses beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's equipped to break down complicated questions and reason through them in a detailed way. This assisted thinking process allows the model to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, rational thinking and data interpretation tasks.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, bio.rogstecnologia.com.br making it possible for effective inference by routing queries to the most relevant specialist "clusters." This approach permits the model to specialize in various issue domains while maintaining total efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as a teacher design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile