Comparing the end-to-end latency of DeepSeek-R1 models to other models on Amazon SageMaker involves evaluating several factors, including model size, hardware configuration, and specific use cases. Here's a detailed overview:
DeepSeek-R1 Models
DeepSeek-R1 models, particularly their distilled variants, are designed to offer efficient performance while maintaining a high level of reasoning capabilities. These models are available in various sizes, such as 1.5B, 7B, 8B, 14B, 32B, and 70B parameters, allowing users to choose based on their specific requirements and available resources[1][4].
When deployed on SageMaker, these models can leverage features like speculative decoding to reduce latency, especially when using Large Model Inference (LMI) containers[1]. The performance evaluation of DeepSeek-R1 distilled models on SageMaker focuses on metrics such as end-to-end latency, throughput, time to first token, and inter-token latency. However, these evaluations are not optimized for each model and hardware combination, suggesting that users should conduct their own tests to achieve the best performance[1][4].
Comparison with Other Models
DeepSeek-R1 models have been compared to other prominent models like OpenAI's o1 in terms of reasoning capabilities. While DeepSeek-R1 outperforms o1 in many reasoning benchmarks, o1 excels in coding-related tasks[3]. However, specific latency comparisons between DeepSeek-R1 and other models like o1 on SageMaker are not detailed in the available information.
Optimizing Latency on SageMaker
To minimize latency for models like DeepSeek-R1 on SageMaker, several strategies can be employed:
- Load Aware Routing: This feature allows SageMaker to route requests to instances with the least load, reducing latency by up to 20% compared to random routing[2].
- Session Routing (Sticky Routing): This ensures that requests from the same session are routed to the same instance, improving performance by reusing previously processed information[2].
- Least Outstanding Requests (LOR) Routing: This strategy optimizes latency by directing requests to instances with the fewest outstanding requests, which can be particularly beneficial for real-time inference workloads[8].
Conclusion
While specific end-to-end latency comparisons between DeepSeek-R1 and other models on SageMaker are not provided, DeepSeek-R1 models offer competitive reasoning capabilities with optimized performance through distilled variants. By leveraging SageMaker's routing strategies and optimizing model deployment, users can achieve lower latency and improved throughput for their AI applications.
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://www.youtube.com/watch?v=4B4tys4-0Vw
[3] https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1
[4] https://aws.amazon.com/blogs/machine-learning/optimize-hosting-deepseek-r1-distilled-models-with-hugging-face-tgi-on-amazon-sagemaker-ai/
[5] https://www.reddit.com/r/MachineLearning/comments/1bjn9dq/d_aws_sagemaker_warm_start_latencies_serverless/
[6] https://www.reddit.com/r/singularity/comments/1icwl73/notes_on_deepseek_r1_just_how_good_it_is_compared/
[7] https://www.flotorch.ai/blog/speed-up-rag-experiments-on-aws-sagemaker-with-deepseek-r1-flotorch
[8] https://aws.amazon.com/blogs/machine-learning/minimize-real-time-inference-latency-by-using-amazon-sagemaker-routing-strategies/