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https://aibrix.github.io/posts/2025-03-10-deepseek-r1

DeepSeek-R1 671B multi-host Deployment in AIBrix

This blog post introduces deploying DeepSeek R1 using AIBrix. DeepSeek-R1 demonstrates remarkable proficiency in reasoning tasks through step-by-step training process. It features 671B total parameters with 37B active parameters, and 128k context length. However, due to its large size, the deployment process is more complex. AIBrix provides enough tools that enables users to deploy and manage distributed inference services efficiently. ref: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/main/figures/benchmark.jpg Prerequisites Before deploying DeepSeek-R1 in AIBrix, some preliminary tasks such as downloading model weights to object storage or a shared file system and setting up a customized container image must be completed. This blog will focus on the critical steps rather than covering all details. You can check our code samples and tutorial for more details.



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DeepSeek-R1 671B multi-host Deployment in AIBrix

https://aibrix.github.io/posts/2025-03-10-deepseek-r1

This blog post introduces deploying DeepSeek R1 using AIBrix. DeepSeek-R1 demonstrates remarkable proficiency in reasoning tasks through step-by-step training process. It features 671B total parameters with 37B active parameters, and 128k context length. However, due to its large size, the deployment process is more complex. AIBrix provides enough tools that enables users to deploy and manage distributed inference services efficiently. ref: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/main/figures/benchmark.jpg Prerequisites Before deploying DeepSeek-R1 in AIBrix, some preliminary tasks such as downloading model weights to object storage or a shared file system and setting up a customized container image must be completed. This blog will focus on the critical steps rather than covering all details. You can check our code samples and tutorial for more details.



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https://aibrix.github.io/posts/2025-03-10-deepseek-r1

DeepSeek-R1 671B multi-host Deployment in AIBrix

This blog post introduces deploying DeepSeek R1 using AIBrix. DeepSeek-R1 demonstrates remarkable proficiency in reasoning tasks through step-by-step training process. It features 671B total parameters with 37B active parameters, and 128k context length. However, due to its large size, the deployment process is more complex. AIBrix provides enough tools that enables users to deploy and manage distributed inference services efficiently. ref: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/main/figures/benchmark.jpg Prerequisites Before deploying DeepSeek-R1 in AIBrix, some preliminary tasks such as downloading model weights to object storage or a shared file system and setting up a customized container image must be completed. This blog will focus on the critical steps rather than covering all details. You can check our code samples and tutorial for more details.

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      This blog post introduces deploying DeepSeek R1 using AIBrix. DeepSeek-R1 demonstrates remarkable proficiency in reasoning tasks through step-by-step training process. It features 671B total parameters with 37B active parameters, and 128k context length. However, due to its large size, the deployment process is more complex. AIBrix provides enough tools that enables users to deploy and manage distributed inference services efficiently. ref: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/main/figures/benchmark.jpg Prerequisites Before deploying DeepSeek-R1 in AIBrix, some preliminary tasks such as downloading model weights to object storage or a shared file system and setting up a customized container image must be completed. This blog will focus on the critical steps rather than covering all details. You can check our code samples and tutorial for more details.
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      This blog post introduces deploying DeepSeek R1 using AIBrix. DeepSeek-R1 demonstrates remarkable proficiency in reasoning tasks through step-by-step training process. It features 671B total parameters with 37B active parameters, and 128k context length. However, due to its large size, the deployment process is more complex. AIBrix provides enough tools that enables users to deploy and manage distributed inference services efficiently. ref: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/main/figures/benchmark.jpg Prerequisites Before deploying DeepSeek-R1 in AIBrix, some preliminary tasks such as downloading model weights to object storage or a shared file system and setting up a customized container image must be completed. This blog will focus on the critical steps rather than covering all details. You can check our code samples and tutorial for more details.
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