DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by routing queries to the most relevant expert “clusters.” This approach allows the model to specialize in different problem domains while maintaining overall efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48xlarge instance to deploy the model. ml.p5e.48xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory."
DraganSr
web-links (blinks) web-log (blog) by Dragan Sretenovic
Thursday, February 20, 2025
AI: DeepSeek-R1@ Amazon Bedrock & SageMaker
"DeepSeek-R1 is a large language model (LLM) developed by DeepSeek-AI that uses reinforcement learning to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing feature is its reinforcement learning (RL) step, which was used to refine the model’s responses beyond the standard pre-training and fine-tuning process.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by routing queries to the most relevant expert “clusters.” This approach allows the model to specialize in different problem domains while maintaining overall efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48xlarge instance to deploy the model. ml.p5e.48xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory."
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by routing queries to the most relevant expert “clusters.” This approach allows the model to specialize in different problem domains while maintaining overall efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48xlarge instance to deploy the model. ml.p5e.48xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory."
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