Deploying DeepSeek-R1 on AWS requires significant GPU resources due to its large size and computational demands. Here are the specific GPU requirements and considerations for deploying DeepSeek-R1 models on AWS:
1. Model Size and VRAM Requirements: The full DeepSeek-R1 model has 671 billion parameters, requiring substantial VRAM. For native FP8 support, the VRAM requirement is approximately 700 GB. However, if the model is converted to BF16 (which is supported by GPUs like the NVIDIA A100), the VRAM requirement increases to about 1.4 TB due to the higher precision format[2][4].
2. Recommended GPU Configuration: For the full DeepSeek-R1 model, a multi-GPU setup is necessary. AWS instances like the `p4d.24xlarge` or `p5.24xlarge`, which are equipped with NVIDIA A100 GPUs, are suitable. These instances provide the necessary VRAM and compute power for large-scale models. For BF16 computation, a setup with 16 NVIDIA A100 GPUs (each with 80 GB of VRAM) is recommended[1][2].
3. AWS Instance Options: AWS offers several instances that can support the deployment of DeepSeek-R1 models, including the `p4d.24xlarge` and `p5.24xlarge`. These instances provide high-performance computing capabilities with NVIDIA A100 GPUs, which are ideal for large-scale AI models like DeepSeek-R1[7].
4. Quantization and Distributed Computing: To reduce VRAM requirements and improve efficiency, quantization techniques can be applied. For example, using 4-bit quantization can reduce the VRAM needs significantly, allowing for deployment on fewer GPUs. Additionally, distributed computing frameworks can be utilized to spread the workload across multiple instances, enhancing scalability and performance[4].
5. Compute and Memory Requirements: Beyond GPU requirements, deploying DeepSeek-R1 also necessitates sufficient CPU memory and overall system resources. AWS instances with ample CPU memory, such as the `g6e.4xlarge`, can support the additional computational needs of these models[6].
In summary, deploying DeepSeek-R1 on AWS requires a robust GPU setup, preferably with NVIDIA A100 GPUs, and potentially leveraging quantization and distributed computing to optimize resource utilization.
Citations:
[1] https://dev.to/askyt/deepseek-r1-671b-complete-hardware-requirements-optimal-deployment-setup-2e48
[2] https://www.theriseunion.com/en/blog/DeepSeek-V3-R1-671B-GPU-Requirements.html
[3] https://www.byteplus.com/en/topic/385683
[4] https://apxml.com/posts/gpu-requirements-deepseek-r1
[5] https://www.youtube.com/watch?v=5RhPZgDoglE
[6] https://community.databricks.com/t5/machine-learning/understanding-compute-requirements-for-deploying-deepseek-r1/td-p/109187
[7] https://aws.amazon.com/blogs/aws/deepseek-r1-models-now-available-on-aws/
[8] https://www.reddit.com/r/ollama/comments/1ifa93h/deepseek_r1_hardware_requirements_explained/