Optimizing the performance of DeepSeek-R1 on AWS using GPU instances involves several key strategies:
1. Choosing the Right GPU Instance**
For optimal performance with DeepSeek-R1, it is crucial to select a GPU-based instance type that provides sufficient memory and compute power. The ml.p5e.48xlarge instance is highly recommended as it comes with 8 Nvidia H200 GPUs, offering 1128 GB of GPU memory, which is essential for handling large models like DeepSeek-R1[9]. Other instances such as ml.g6e.12xlarge and ml.g6e.48xlarge have also shown excellent performance for various DeepSeek-R1 distilled models[1].2. Model Sharding Across GPUs**
When using instances with multiple GPUs, sharding the model across all available GPUs can significantly improve performance. This allows the model to be distributed and processed in parallel, enhancing throughput and reducing latency[1].3. Optimizing Model Configuration**
Using the Large Model Inference (LMI) container with optimized parameters can help in achieving better performance. For example, setting `MAX_MODEL_LEN` to a suitable value can ensure efficient handling of long input sequences without chunking or prefix caching[1].4. Concurrency and Batch Size**
Increasing concurrency and using larger batch sizes can improve throughput, especially in real-time inference scenarios. However, it's important to balance concurrency with available resources to avoid overloading the instance[1].5. Software Optimizations**
Utilizing software optimizations available in frameworks like NVIDIA NIM can further enhance performance. These optimizations can simplify deployments and ensure high efficiency in agentic AI systems[4].6. Monitoring and Testing**
Always perform thorough testing with your specific dataset and traffic patterns to identify the optimal configuration for your use case. This includes evaluating end-to-end latency, throughput, time to first token, and inter-token latency[1].7. Cost Efficiency**
While focusing on performance, consider cost efficiency by leveraging Savings Plans or Spot Instances for non-real-time tasks. This can help balance performance needs with budget constraints[3].By implementing these strategies, you can effectively optimize the performance of DeepSeek-R1 on AWS using GPU instances.
Citations:
[1] https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-models-on-amazon-sagemaker-using-a-large-model-inference-container/
[2] https://aws.amazon.com/blogs/aws/deepseek-r1-models-now-available-on-aws/
[3] https://community.aws/content/2rhRJI6cxBa1Ib5f3TjsfPadpXs/deploying-deepseek-r1-distill-llama-70b-for-batch-inference-on-aws-graviton4?lang=en
[4] https://blogs.nvidia.com/blog/deepseek-r1-nim-microservice/
[5] https://community.aws/content/2sEuHQlpyIFSwCkzmx585JckSgN/deploying-deepseek-r1-14b-on-amazon-ec2?lang=en
[6] https://vagon.io/blog/a-step-by-step-guide-to-running-deepseek-r1-on-vagon-cloud-desktops
[7] https://aws.amazon.com/blogs/machine-learning/optimize-hosting-deepseek-r1-distilled-models-with-hugging-face-tgi-on-amazon-sagemaker-ai/
[8] https://www.reddit.com/r/aws/comments/1i8v9w5/scalable_deepseek_r1/
[9] https://aws.amazon.com/blogs/machine-learning/deepseek-r1-model-now-available-in-amazon-bedrock-marketplace-and-amazon-sagemaker-jumpstart/
[10] https://community.aws/content/2Z6DlAohx12yuNoEAs7qb5YTH0q/leveraging-deepseek-r1-on-aws?lang=en